Patent Application
20210340499
The Sequence Listing, which is a part of the present disclosure, is submitted concurrently with the specification as a text file. The name of the text file containing the Sequence Listing is “53514A_Seqlisting.txt”, which was created on Sep. 24, 2019 and is 3,474 bytes in size. The subject matter of the Sequence Listing is incorporated herein in its entirety by reference.
The disclosure relates generally to the fields of cell biology and organogenesis, and more particularly, to methods of generating or enriching for pancreatic endocrine progenitor cells, such as those progenitors that give rise to beta cells capable of controlled insulin production.
Pancreatic organogenesis is a complex and dynamic process that ultimately results in the generation of multiple cell lineages that perform the functions of the mature organ: the regulation of glucose homeostasis by the endocrine compartment and the production of digestive enzymes by the exocrine compartment. In the mouse, all known epithelial lineages of the pancreas derive from a small field of epithelial precursor cells within the foregut endoderm specified by the expression of pancreatic duodenal transcription factor 1 (Pdx1) (
Although the pancreatic mesenchyme is required for the proper differentiation, proliferation, and morphogenesis of the epithelial network1, little is known about the cell identities and lineages that compose the pancreatic mesenchyme during development. Even less is known about the mechanisms by which these distinct mesenchymal cell types interact with one another and with the cells of the epithelial compartment during development and in the adult organ. Therefore, a deeper understanding of the full diversity of the mesenchymal cell types, as well as their global gene expression profiles, will serve as the basis for understanding these key cellular interactions.
The successful production of glucose-sensitive, insulin-producing beta cells was a major leap forward, but there are several key limitations that remain before hESC-derived beta cells can be used as a therapeutic intervention for diabetes. First, the number of beta cells required for transplantation into one diabetic patient is on the order of one billion (Jacobson and Tzanakakis, 2017; Lock and Tzanakakis, 2007). Although current hESC-derived beta cell differentiation protocols are capable of differentiating millions of hESCs at a time, the efficiency of generating beta cells is batch-dependent and low, at approximately 30-40% (Pagliuca et al., 2014; Rezania et al., 2014; Russ et al., 2015) and even reported purification methods are low-throughput and labor-intensive (Nair et al., 2019; Veres et al., 2019).
Improvements in the efficiency of functional beta cell production are needed for these regenerative-based therapies to be scalable and of consistent quality. These challenges in beta cell functionality and differentiation efficiency in hESC-derived beta cell differentiation protocols can be addressed by better understanding the gene expression programs that drive beta cell differentiation and maintain beta cell identity in vivo. Currently available methods may be failing to activate other transcriptional programs not yet uncovered to be required for proper beta cell differentiation and function. Additionally, although current differentiation protocols for generating hESC-derived beta cells recapitulate key developmental stages and genetic programs used to make beta cells in vivo, the programs may not fully mimic the exact developmental path taken in vivo in in vitro approaches.
Thus, existing directed differentiation protocols to generate beta cells from hESCs fail to produce sufficient numbers of functional beta cells that maintain glucose-sensing, insulin-secreting capabilities over time. Therefore, a need continues to exist in the art for pancreatic beta cells capable of controllable or regulated insulin production and methods of producing such pancreatic beta cells, as well as methods of enriching for such beta cells, including autologous pancreatic beta cells.
Disclosed herein is the discovery of an endocrine progenitor cell population, marked by differential expression of a transcription factor named Fev (or Pet1), that gives rise to endocrine cells, including insulin-producing beta cells, in mouse pancreatic development. This Fev+ population has also been identified in human fetal pancreata during stages at which beta cell differentiation occurs, indicating that this Fev+ population is relevant to not only mouse, but also human beta cell development. In addition, we have found a Fev-expressing cell population present in our in vitro platform for performing directed differentiation of human embryonic stem cells (hESCs) to insulin-producing beta cells. Also disclosed herein is a Fev-reporter hESC line useful in enriching for this Fev+ endocrine progenitor population during directed differentiations of hESCs to beta cells.
The findings disclosed herein have been extended to the postnatal period, when beta cells undergo massive expansion, and when we also identified FevHigh (FevHI) cells in pancreatic islets. Although the current model in the field is that new beta cells arise by duplication of pre-existing beta cells, we have demonstrated with genetic fate mapping experiments that these FevHigh islet cells give rise to insulin-producing beta cells. This raises the distinct possibility that the FevHI post-natal endocrine progenitor population may represent a novel source of beta cells after birth—during homeostasis and/or during injury or disease.
Recent studies of late embryonic, postnatal, and adult alpha and beta cells have demonstrated the power of single-cell transcriptomic profiling for unraveling endocrine lineage heterogeneity and revealing distinct transcriptional states of beta cell maturation3-5. Here, we perform droplet-based, single-cell RNA sequencing of entire murine embryonic pancreata at earlier developmental time points to describe the cellular diversity and dynamics of gene expression in both the epithelial and mesenchymal compartments. We further validate the existence of novel populations within mouse and human pancreatic tissue, as well as human embryonic stem cell (hESC)-derived endocrine progenitor cells. Finally, we predict novel lineage relationships, identify previously unappreciated intermediate progenitor cells, and validate our methodology using in vivo genetic lineage tracing.
In one aspect, the disclosure provides a method of enriching the pancreatic endocrine progenitor cell population in a cell sample comprising (a) detecting cells in the sample expressing a pancreatic endocrine progenitor cell marker; and (b) separating a pancreatic endocrine progenitor cell from at least one cell that does not express the pancreatic endocrine progenitor cell marker, thereby enriching the pancreatic endocrine progenitor cell population of the cell sample. In some embodiments, the pancreatic endocrine progenitor cell is a human cell. In some embodiments, the pancreatic endocrine progenitor cell is an alpha cell progenitor, a beta cell progenitor, a delta cell progenitor, a PP cell progenitor, or an epsilon cell progenitor. In some embodiments, the pancreatic endocrine progenitor cell marker is the E26 transformation-specific transcription factor Fev. In some embodiments, the pancreatic endocrine progenitor cell is a beta cell progenitor, such as a Fev+ beta cell progenitor. In some embodiments, the Fev+ beta cell progenitor further comprises Gng12+, Tssc4+, Ece1+, Tmcm108+, Wipi1+, or Papss2+. In some embodiments, the beta cell progenitor is Fev+, Gng12+. In some embodiments, the Fev+ beta cell progenitor further comprises Pax4+, Chga+, Chgb+, Neurod1+, Runx1t1+, or Vim+. In some embodiments, the Fev+ beta cell progenitor does not express detectable Ngn3, Ins1 or Gcg. In some embodiments, the beta cell progenitor is Fev+, Ngn−. In some embodiments, the Fev+, Ngn− beta cell progenitor expresses a gene in the serotonin pathway, the insulin signaling pathway, sphingosine-1-phosphate signaling pathway, or Activating Transcription Factor-2. In some embodiments, the Fev+ beta cell progenitor further comprises Pdx1+ or Mafb+. In some embodiments, the at least one cell that does not express the pancreatic endocrine progenitor cell marker is a CD140+ mesenchyme cell. In some embodiments, the beta cell progenitor cell is a human cell.
Another aspect of the disclosure is a method of producing a pancreatic endocrine progenitor cell comprising culturing a stem cell under conditions that induce differentiation of the stem cell into a pancreatic endocrine progenitor cell. In some embodiments, the stem cell is an embryonic stem cell (ESC) or an inducible pluripotent stem cell (iPSC). In some embodiments, the pancreatic endocrine progenitor cell is an alpha cell progenitor, a beta cell progenitor, a delta cell progenitor, a PP cell progenitor, or an epsilon cell progenitor. In some embodiments, the pancreatic endocrine progenitor cell marker is the E26 transformation-specific transcription factor Fev, such as a Fev+ beta cell progenitor. In some embodiments, the pancreatic endocrine progenitor cell is a beta cell progenitor. In some embodiments, the Fev+ beta cell progenitor further comprises Gng12+, Tssc4+, Ece1+, Tmcm108+, Wipi1+, or Papss2+. In some embodiments, the beta cell progenitor is Fev+, Gng12+. In some embodiments, the Fev+ beta cell progenitor further comprises Pax4+, Chga+, Chgb+, Neurod1+, Runx1t1+, or Vim+. In some embodiments, the Fev+ beta cell progenitor does not express detectable Ngn3, Ins1 or Gcg. In some embodiments, the beta cell progenitor is Fev+, Ngn−. In some embodiments, the Fev+, Ngn− beta cell progenitor expresses a gene in the serotonin pathway, the insulin signaling pathway, sphingosine-1-phosphate signaling pathway, or Activating Transcription Factor-2. In some embodiments, the Fev+ beta cell progenitor further comprises Pdx1+ or Mafb+. In some embodiments, the Fev+ beta cell progenitor is a human cell.
Yet another aspect of the disclosure is an isolated Fev+ pancreatic endocrine progenitor cell produced according to the methods disclosed herein. An exemplary Fev+ pancreatic endocrine progenitor cell produced according to the methods disclosed herein is an isolated Fev+ beta cell progenitor.
Still another aspect of the disclosure is a method of inducing formation of a hormone-producing cell comprising contacting a progenitor of a hormone-producing cell with an effective amount of Fev to produce a hormone-producing cell. In some embodiments, the hormone-producing cell is an INS+ cell. In some embodiments, the hormone-producing progenitor cell is an ES4 cell. In some embodiments, the hormone-producing cell is a beta cell. In some embodiments thereof, the hormone-producing progenitor cell is a beta-like cell, for example a beta-like cell at the end stage of the directed differentiation of hESCs to the beta cell lineage. In some embodiments, the method is performed in vitro. In some embodiments, the method further comprises removing a cell expressing at least one of PHOX2A, TLX2 or TBX2.
Another aspect of the disclosure is a method of screening for a signaling compound that induces FEV+ progenitor cell replication comprising: (a) contacting a FEV+ progenitor cell with a candidate compound; (b) culturing the FEV+ progenitor cell under conditions suitable for cell proliferation; (c) measuring the cell proliferation of the FEV+ progenitor cell in the presence or absence of the candidate compound; and (d) identifying the compound as a signaling compound for FEV+ progenitor cell proliferation if the cell proliferation in the presence of the compound is greater than the cell proliferation in the absence of the compound. In some embodiments, the FEV+ progenitor cell is a FEV-MYC progenitor cell, a FEV-GFP progenitor cell, a FEV-KO progenitor cell, or a FEV-tNFGR progenitor cell.
In still another aspect, the disclosure provides a method of screening for a signaling compound that enhances FEV+ progenitor cell differentiation into beta cells comprising: (a) contacting FEV+ progenitor cells with a candidate compound; (b) incubating the FEV+ progenitor cells under conditions suitable for cell differentiation; (c) measuring the level of differentiation of the FEV+ progenitor cells to beta cells in the presence or absence of the candidate compound; and (d) identifying the compound as a signaling compound for FEV+ progenitor cell differentiation into beta cells if the cell differentiation in the presence of the compound is greater than the cell differentiation in the absence of the compound. In some embodiments, the FEV+ progenitor cell is a FEV-MYC progenitor cell, a FEV-GFP progenitor cell, a FEV-KO progenitor cell, or a FEV-tNFGR progenitor cell.
Other features and advantages of the disclosure will be better understood by reference to the following detailed description, including the drawing and the examples.
Organogenesis requires the complex interactions of multiple cell lineages that coordinate their expansion, differentiation, and maturation over time. Utilizing a combination of single-cell RNA sequencing, immunofluorescence, in situ hybridization, and genetic lineage tracing, we profile the cell types within the epithelial and mesenchymal compartments of the murine pancreas across developmental time. We identify previously underappreciated cellular heterogeneity of the developing mesenchyme and reconstruct potential lineage relationships among the pancreatic mesothelium and novel mesenchymal cell types. Within the epithelium, we find a novel endocrine progenitor population, as well as an analogous population in both human fetal tissue and human embryonic stem cells differentiating towards a pancreatic beta cell fate. Further, we identify candidate transcriptional regulators along the differentiation trajectory of this population towards the alpha or beta cell lineages. This work establishes a roadmap of pancreatic development and demonstrates the broad utility of this approach for understanding lineage dynamics in developing organs.
The mesenchyme is critical for epithelial specification and proliferation throughout pancreatic development48-50, yet the individual cell types responsible for these processes remain unidentified. Our single-cell dataset has enabled the identification of multiple novel mesenchymal populations, highlighted the transcriptional dynamism of the pancreatic mesothelium, and predicted lineage relationships among the mesothelium and VSM populations. Secreted factors, such as mesothelial-derived Fgf9, may play a similar role in the pancreas as in the lung, where it regulates mesenchymal cell proliferation and vascular formation51. While previous studies identified Cxcl12 (highly expressed in our dataset in cluster 4) as a regulator of pancreatic epithelial specification, differentiation, and adult regeneration52,53, these studies focused on the epithelium and did not define a role for mesenchymally-derived Cxci12. Finally, secretion of Wnt antagonists by cluster 5 may regulate processes regulated by Wnt signaling in the developing pancreas, including epithelial specification, expansion, and exocrine development54. Future work can focus on uncovering the functions of these individual mesenchymal populations in development, physiology, and pathology of the pancreas.
With the various cell types of the mesenchyme now enumerated and their markers identified, we can begin to elucidate the maturation and lineage relationships of the pancreatic mesenchymal compartment. Our time course data have provided evidence of maturation within the mesothelial population. Genes such as Pitx2, kallikren 13 (Kik13) and 8 (Kik8), were differentially expressed in younger, E12.5, mesothelial cells. Pitx2 regulates differentiation in multiple systems27,57-60, and the kallikren family are serine proteases that are involved in extracellular matrix and adhesive molecule degradation55. Expression of these genes leads to the expectation that the E12.5 mesothelial population is primed for migration and differentiation. In contrast, the E17.5 mesothelial population expressed genes related to barrier or immune function, such as dermokine (Dmkn)56,57, bone marrow stromal antigen 2 (Bst2), and retinoic acid receptor responder 2 (Rarres2)58. These results establish stage-dependent roles for the mesothelium throughout development.
The different roles for the mesothelium across time are also evident from our pseudotime analysis, which predicts that the mesothelium serves as a progenitor of other mesenchymal cell types during development. Indeed, the mesothelium is a critical mesenchymal progenitor population in other organs, such as the heart, intestine, lung, and liver14-17. The data disclosed herein indicate that mesothelial progenitor activity occurs at E12.5 or earlier during pancreatic development, consistent with other organ systems11,14,16. Indeed, a recent study identified that parietal mesothelial cells can function as progenitor cells prior to pancreatic specification59. In vivo lineage tracing studies will verify the predictions from these pseudotime analyses, and the transcriptomic information obtained by this study will allow the development of tools to target individual populations within the mesenchyme and perform lineage tracing, ablation, and expression studies.
The study of the mesothelium in development is also relevant for fibrotic diseases of adult organs, as factors secreted by mesothelial cells and mesothelial-derived, disease-driving myofibroblasts modulate organ responses to injury60-62. Fibrotic diseases of the adult pancreas are characterized by aberrant recapitulation of developmental pathways within the epithelium63,64. We can now utilize our developmental dataset to probe the mesenchymal populations during adult homeostasis and disease states, and compare to the populations detected throughout development. Therefore, this dataset serves as a broad resource for the implementation of future studies in pancreatic mesenchymal biology.
Within the epithelial compartment, our identification of a novel FevHi endocrine progenitor population provides increased resolution of endocrine differentiation. The relative timing of expression of canonical endocrine lineage genes can now be mapped onto these additional differentiation stages. Several lines of evidence identify the gene Fev as a direct target of Ngn3: Fev is the transcription factor most strongly expressed in Ngn3+ endocrine progenitors65, and Ngn3 knockout embryos do not exhibit Fev expression in the developing pancreas24. Known target genes of Ngn3, such as Pax466 and Runx1t1i67, are expressed by the early-stage Fev+/Pax4+ population. Additionally, Pax6 was upregulated within the FevHi population. Although Chga and Chgb are often utilized as markers of differentiated endocrine lineages, we found that Chga and Chgb are expressed in the FevHi population prior to hormone acquisition. This result is consistent with previous work that identified Chga+, hormone− cells in rodent pancreatic development68. The FevHi cell stage likely represents the cell stage during endocrine differentiation preceding specialized hormone production and may now serve as a cellular landmark for understanding endocrine lineage gene expression dynamics.
The gene Fev has been previously studied mainly in serotonergic neurons, where it is a master transcription factor required for cellular differentiation and maturation, as well as serotonin synthesis28. Fev switches transcriptional targets from differentiation genes during development to maturation genes postnatally in serotonergic neurons69. In an insulinoma cell line, Fev directly binds to the regulatory regions of serotonergic genes, such as Tph1, Tph2, Ddc, Slc18a2, and Slc6a4, as well as the Ins1 promoter itself24. Future ChIP-seq studies of embryonic pancreas will globally identify direct targets of Fev and Fev-regulated transcriptional networks in developing endocrine cells.
Using genetic lineage tracing in vivo, we have demonstrated that all five endocrine lineages of the developing pancreas transit through a Fev-expressing stage, and that Fev− lineage cells contribute not only to embryonic, but also to adult pancreatic endocrine cells. The fraction of epsilon cells that are not derived from a Fev− lineage may represent the subset of ghrelin+ cells previously reported to give rise to cells of the ductal and exocrine lineages 30. Given that all adult gamma cells are Fev− lineage labeled, the small subset of gamma cells that are not lineage traced during pancreatic development may represent those that do not persist in the adult pancreas. Further highlighting the relevance of FevHi progenitors during pancreatic development, our pseudotime analysis revealed that Fev-expressing cells may be pre-specified towards an alpha or beta cell fate (
For the eventual application of this knowledge to human therapeutics, it is important to validate that the predicted relationships hold true in the context of human pancreatic development. Our staining of human fetal pancreas identified the analogous FEVHi population, consistent with our findings in murine pancreata. Directed differentiation of hESCs towards endocrine cell fates will provide a platform for modeling and manipulating the predicted lineage regulators found in this study. Indeed, we have identified a FEV+ population within hESC-derived endocrine progenitor cells. Deeper knowledge of these lineage decisions may substantially improve directed differentiation efforts to efficiently generate functional beta cells for cellular replacement therapy for patients with diabetes. This study highlights the power of combining single-cell transcriptomic information with in vivo lineage tracing to reconstruct developmental trajectories within cellular compartments. Identification of novel populations and their lineage relationships will promote discovery of the mechanisms that drive lineage decisions and commitment.
The following examples are presented by way of illustration and are not intended to limit the scope of the subject matter disclosed herein.
Materials and Methods
Mice
All mouse procedures were approved by the University of California, San Francisco (UCSF) Institutional Animal Care and Use Committee (IACUC). Mice were housed in a 12-hour light-dark cycle in a controlled temperature climate. Noon of the day of vaginal plug was considered embryonic day 0.5.
Timed-pregnant Swiss Webster mice were obtained from Charles River Laboratories. Ngn3-Cre70, Fev-Cre71, ROSA26mTmG 31 mice have been previously described and were maintained in a C57BL/6J background.
Human Tissue Procurement and Isolation
Human fetal pancreata were harvested from post-mortem fetuses at 23 weeks of gestation with permission from the ethical committee of the University of California, San Francisco (UCSF). Tissue was fixed in 4% paraformaldehyde overnight at 4° C. After three washes in 1×PBS, tissue was either cryopreserved in 30% sucrose solution at 4° C. overnight and embedded in OCT, or placed in 40% ethanol then 70% ethanol before paraffin embedding. 8 um sections were cut on the cryostat or microtome. In situ hybridization and immunofluorescence were then performed as described below.
Adult human islets were isolated from cadaveric donor tissue by the UCSF Islet Production Core with permission from the UCSF ethical committee. Consented cadaver donor pancreata were provided by the nationally recognized organization UNOS via local organ procurement agencies. The identifiers were maintained at the source only, and the investigators received de-identified specimens.
Informed consent was obtained for all human (fetal and adult) tissue collection, and protocols were approved by the Human Research Protection Program Committee on Human Research of the University of California, San Francisco (UCSF).
Embryonic Stem Cell Culture and Differentiation to the Endocrine Lineage
The human embryonic stem cell (hESC) line HUES8 was obtained from Harvard University and used for the generation of hESC-derived β-like cells (BLCs). Pluripotent HUES8 cells were maintained as spherical clusters in suspension in mTeSR-1 (StemCell Technologies) in 500 mL spinner flasks (Corning, VWR) on a magnetic stir plate (Dura-Mag) within a 37° C. incubator with 5% CO2, 100% humidity, and a rotation rate of 70 rpm. Cells were screened for mycoplasma contamination using the MycoProbe Mycoplasma Detection Kit (R&D Systems), according to the manufacturer's instructions.
hESC-derived endocrine progenitor cells were generated as previously described32. In brief, HUES8 cells were seeded into a spinner flask at a concentration of 8×105 cells/mL in mTeSR-1 media with 101.iM Rock inhibitor Y27632 to allow formation of spherical clusters. Differentiation was initiated 72 hours later. Differentiation was achieved in a step-wise fashion using the following growth factors and/or small molecules: definitive endoderm cells (Stage 1) (Activin A 100 ng/mL, R&D Systems; CHIR99021 141.ig/mL, Stemgent); gut tube endoderm cells (Stage 2) (KGF 50 ng/mL, Peprotech); early pancreatic progenitors (Stage 3) (LDN193189 200 nM, Fisher Scientific; KGF 50 ng/mL, Peprotech; Sant-1 0.251.iM, Sigma; Retinoic Acid 21.iM, Sigma; PdbU 500 nM, EMD Biosciences); later pancreatic progenitors (Stage 4) (KGF 50 ng/mL, Peprotech; Sant-1 0.251.iM, Sigma; Retinoic Acid 0.11.iM, Sigma); endocrine progenitors (Stage 5) (Sant-1 0.251.iM, Sigma; Retinoic Acid 0.11.iM, Sigma; XXI 11.iM, EMD Millipore; Alk5i 101.iM, Axxora; T3 11.iM, EMD Biosciences; Betacellulin 20 ng/mL, Fisher Scientific), BLCs (Stage 6) (Alk5i; T3). Successful differentiation was assessed at the definitive endoderm, pancreatic progenitor 1, pancreatic progenitor 2, and endocrine progenitor stages via immunofluorescence or FACS for stage-specific marker genes.
To measure the expression of FEV at various stages of human endocrine differentiation, aliquots of clusters were removed from the flask and analyzed at several time points: after 5 days in Stage 5 (“mid-stage endocrine progenitors”), after 7 days in Stage 5 (“late-stage endocrine progenitors”), and after 5 days at the BLC stage. As a comparator, pluripotent, undifferentiated hESCs in mTeSR-1, as well as human adult islets, were also analyzed for FEV expression.
Immunofluorescence
Embryonic mouse pancreata were dissected in cold 1×PBS and fixed in zinc-buffered formalin (Anatech LTD) at room temperature (RT) for 30-90 minutes or overnight at 4° C. After three washes in 1×PBS, tissue was processed for either cryopreservation or paraffin embedding. Cryopreserved pancreata were placed in 30% sucrose solution at 4° C. overnight before embedding in OCT. Paraffin-embedded pancreata were placed in 40% ethanol and 70% ethanol before paraffin tissue processing. 8 um sections were cut on the cryostat or microtome. For immunofluorescence on paraffin sections, slides were baked at 55° C. for 30 minutes, deparaffinized in xylene, and rehydrated in decreasing concentrations of ethanol. Heat-mediated antigen retrieval was performed using Antigen Retrieval Citra Solution (Biogenex Laboratories). Tissue sections were blocked in 5% normal donkey serum (NDS; Rockland Immunochemicals) and Mouse-on-Mouse IgG blocking reagent (Vector Laboratories) when appropriate in 0.2% Triton X-100 in PBS (PBT) for 1 hour and then stained overnight at 4° C. using the following primary antibodies: Acta2 (1:200, Abcam ab21027), Cav1 (1:200, Abcam ab2910), Chromogranin A (1:100, Abcam ab15160), E-cadherin (1:200, BD Transduction Lab 610182), Glucagon (1:100, Abcam ab82270), Insulin (1:50, DAKO A0564), Vimentin (1:200, Abcam ab92547), and Wt1 (1:100, Abcam ab89901). All antibodies have been validated by the manufacturer. The next day, sections were washed three times in 0.1% Tween 20 in 1×PBS and then incubated with species-specific Alexa Fluor 488-, 594-, or 647-conjugated secondary antibodies (1:500, Jackson ImmunoResearch) and DAPI in 5% NDS in 0.2% PBT for 1 hour at RT. Sections were washed three times in 0.1% Tween 20 in 1×PBS, rinsed in 1×PBS, and then mounted in Fluoromount-G mounting medium (Southern Biotech). Slides were stored at 4° C.
For immunofluorescence on cryosections, slides were removed from −80° C. storage and allowed to reach RT. Sections were rinsed in 1×PBS three times and permeabilized in 0.5% PBT for 10 minutes at RT. Tissue sections were blocked in 5% NDS and, if needed, Mouse-on-Mouse IgG blocking reagent in 0.1% PBT for 1 hour and then stained overnight at 4° C. using the following primary antibodies: Epcam (1:200, BD Transduction Lab 552370), Glucagon (1:2000, Millipore 4031-01F), Insulin (1:250, DAKO A0564), Somatostatin (1:500, Santa Cruz Biotechnology sc-7819, Ghrelin (1:1500, Santa Cruz Biotechnology sc-10368), Pancreatic Polypeptide (PPY; 1:250, Abcam ab77192), and Vimentin (1:200, Abcam ab92547). All antibodies have been validated by manufacturer. Sections were washed the next day three times in 1×PBS and then incubated with species-specific Alexa Fluor 488-, 555-, 594-, or 647-conjugated secondary antibodies and DAPI in 5% NDS in 0.1% PBT for 1 hour at RT. Sections were washed three times in 1×PBS and mounted in Fluoromount-G mounting medium. Slides were stored at 4° C.
Images were captured on a Zeiss Apotome Widefield microscope with optical sectioning capabilities or Leica confocal laser scanning SP8 microscope. Maximum intensity z-projections were then prepared using ImageJ, where brightness, contrast, and pseudo-coloring adjustments were applied equally across all images in a given series.
In Situ Hybridization
In situ hybridization was performed on 8 um sections as previously describee using RNAscope technology (Advanced Cell Diagnostics)73 according to the manufacturer's instructions. In situ probes against mouse Ngn3 (422409-C2), Fev (413241-C3), Isl1 (451931), Ins1 (414661-C4), Gcg (400601), Sst (404631-C3), Ghrl (415301-C2), Ppy (482701), Peg10 (512921-C4), Gng12 (462521-C2), Nnat (432631-C2), Barx1 (414681), Pitx2 (412841-C2), Stmn2 (498391-C3), Msln (443241) and human NGN3 (505791-C4), FEV (471421-C3), and ISL1 (478591-C2) were used in combination with the RNAscope Multiplex Fluorescent Reagent Kit v2 for target detection. Following signal amplification of the target probes, sections were washed in 1×PBS three times and blocked in 5% NDS in 0.1% PBT for 1 hour at RT. Tissue sections were then stained with primary and secondary antibodies as described above in the “immunofluorescence” section.
For in situ hybridization of hESC-derived clusters, cells were fixed with 4% PFA for 15 minutes at RT, washed with PBS, and cryoprotected in 30% sucrose overnight. The next day, clusters were embedded in a small sphere of 1.5% low-melting temperature agarose; these were again cryoprotected in 30% sucrose overnight. The following day, the agarose spheres were soaked in OCT and frozen in a dry ice bath. In situ hybridization was then performed on 8 um sections using human NGN3, FEV, and ISL1 RNAscope probes.
Quantification of Cell Proportions
Quantification of pancreata was performed by manual counting using ImageJ software. Cell populations present at less than 1% in Ngn3-lineage-traced E14.5 replicates were deemed artifact and excluded from further analysis.
Quantitative RT-PCR
hESCs from various stages of directed differentiation were collected and RNA extracted with the RNeasy Mini Kit (Qiagen). Reverse transcription was performed with the Clontech RT-PCR kit. RT-PCR was run on a 7900HT Fast Real-Time PCR instrument (Applied Biosystems) with Taqman probes for FEV (assay ID: Hs00232733_m1) and GAPDH (assay ID: Hs02758991_g1) in triplicate. Data were normalized to GAPDH. Error bars represent standard deviation.
Dissociation and FACS of Embryonic Pancreas
Embryonic mouse pancreata were dissected and placed in 1×PBS on ice, then dissociated into single cells using TrypLE Express dissociation reagent (Thermo Fisher) at 37° C. with pipet trituration at 5-minute intervals during incubation. For v1 datasets, E12.5 pancreata were dissociated for 10 minutes, E14.5 pancreata for 15 minutes, and E17.5 pancreata for 30 minutes. For batch 1, we pooled 14 E14.5 pancreata from one litter. For batch 2, which was collected on a different day, we pooled tissue from each time point separately: 18 E12.5 pancreata from two litters, 11 E14.5 pancreata from one litter, and 8 E17.5 pancreata from one litter. Dissociations were neutralized with FACS buffer (10% FBS+2 mM EDTA in phenol-red free HBSS). Dissociated cells were passed through a 30 um cell strainer and stained with Sytox live/dead stain (Thermo Fisher). Stained cells were washed twice in FACS buffer and then sorted using a BD FACS Aria II. After size selection to remove doublets, all live cells were collected.
For version 2 10× datasets, we pooled tissue from each time point separately, each performed on a different day: 14 E12.5 pancreata from one litter, 13 E14.5 pancreata from one litter, and 13 E17.5 pancreata from one litter. For the E14.5 Fev-Cre; ROSA26mTmG 10× sample, we pooled 3 pancreata from one litter. Dissociations were performed as described above. Cells undergoing a CD140a negative selection were stained with CD140a-APC (1:50; eBiosciences, cat. 17-1401-81; validated by manufacturer). Stained cells were washed twice in FACS buffer and then sorted using a BD FACS Aria II. After size selection to remove doublets, all live CD140a− cells were collected. For the E14.5 Fev-Cre; mTmG pancreata, live GFP+ cells and GFP−/TdTomato+ cells were collected. All 4,000 GFP+ (Fev-lineage-traced) cells were loaded onto the 10× Genomics platform, supplemented with an additional 21,000 TdTomato+/GFP− (non-lineage-traced).
Single-Cell Capture and Sequencing
To capture individual cells, we utilized the Chromium Single Cell 3′ Reagent Version 1 Kit (10× Genomics)74. For batch 1, 12,800 cells from E14.5 pancreata were loaded into one well of the 10× chip, while for batch 2, 18,000 cells per time point were each loaded into their own respective wells to produce Gel Bead-in-Emulsions (GEMs). GEMs underwent reverse transcription to barcode RNA before cleanup and cDNA amplification. Libraries were prepared with the Chromium Single Cell 3′ Reagent Version 1 Kit. Each sample was sequenced on 2 (Batch 1) or 1 (Batch 2) lanes of the HiSeq2500 (Illumina) in Rapid Run Mode with paired-end sequencing parameters: Read1, 98 cycles; Index1, 14 cycles; Index2, 8 cycles; and Read2, 10 cycles.
The CD140a-depleted E12.5, E14.5, and E17.5 datasets and Fev-Cre; ROSA26mTmG dataset in
Single-Cell Analysis
For the v1 datasets, we utilized CellRanger v1.1.0 software for v1 datasets and v2.1.0 for v2 datasets with default settings for de-multiplexing, aligning reads to the mouse genome (10× Genomics pre-build mm10 reference genome) with STAR′ and counting unique molecular identifiers (UMIs) to build transcriptomic profiles of individual cells. For the v1 datasets, gene barcode matrices were analyzed with the R package Seurat v1.4, using the online tutorial as a guide7,76. We first performed a filtering step, retaining only the cells that expressed a minimum of 200 genes and only the genes that were expressed in at least 3 cells. A large number of cells did not meet this threshold in the E17.5 time point and were determined to be red blood cells by the high expression of hemoglobin genes. Variable genes were determined by mean-variance relationship to identify highly expressed and variable genes with the Seurat function MeanVarPlot with default settings. UMI counts were log-normalized, and linear regression was performed with RegressOut to account for differences in the number of UMIs between cells. Principal component analysis (PCA) was then utilized to determine sources of variability in the dataset with PCAfast. Significant PCs were determined based on the Scree plot and utilized for Seurat's graph-based clustering algorithm (function FindClusters) with default parameters, except for the resolution parameter. To vary cluster numbers, the resolution parameter in FindClusters was adjusted from 0.6-3.0, and resulting clusters analyzed as follows. Clusters were visualized with t-distributed stochastic neighbor embedding (t-SNE) with Seurat's RunTSNE function with default settings77. Differentially expressed genes were determined with the FindAllMarkers function, which uses a bimodal likelihood ratio test8. We confirmed differential gene expression analysis with the Wilcox rank sum test and MAST9 utilizing Seurat v2's FindMarkers function with default settings. These tests calculate adjusted p-values for multiple comparisons. To determine the final number of clusters, clusters were required to have at least 9 significantly (p<0.05) differentially expressed genes with a 2-fold difference in expression in comparison to all other clusters. Clusters were manually curated for differential gene expression, and those that did not meet this threshold were manually merged with the nearest cluster based on the phylogenetic tree from Seurat's BuildClusterTree. In some cases, clusters met the 9-gene threshold but appeared to have very similar differentially expressed genes to another cluster. This is likely a result of the comparison of individual clusters against all other clusters in determining differentially expressed genes. In these cases, a pairwise comparison between the two clusters was performed and the same 9-gene threshold applied. An exception to the 9-gene threshold was made to annotate the proliferating population in early stages of the cell cycle within the E14.5 mesenchymal analysis (
Custom Genome Build
The custom genome for alignment of reads to eGFP and TdTomato sequences from the mTmG mouse line was created according to instructions provided by 10× Genomics reference support (https://support.10×genomics.com/single-cell-gene-expression/software/pipelines/latest/advanced/references). eGFP and TdTomato sequences were concatenated to the mm10-2.1.0 reference genome (FASTA file) provided by 10× Genomics. eGFP and TdTomato annotations were then concatenated to the mm10 annotations (GTF file) provided by 10× Genomics. The Cellranger mkref command was then utilized with the genome and annotations with eGFP and TdTomato, as described in the above link.
Pathway Analysis
Pathway analysis and calculation of associated p-values were performed using the ConsensusPathDB over-representation analysis for pathway-based sets category (http://cpdb.molgen.mpg.de)78.
Aggregating E17.5 v2 Datasets
E17.5 technical replicates from the v2 dataset were aggregated with Cellranger v2.1, utilizing the aggr function with default settings. The aggregated dataset was used for analysis and merging with the E12.5 and E14.5 v2 datasets.
Sub-Clustering and Merging Datasets
Sub-clustering was performed by isolating clusters of interest with the Seurat function SubsetData and reanalyzing as described above. Cells were classified as epithelial based on the expression of E-cadherin (Cdh1) and other known epithelial population markers. Cells that were Cdh1−; Vim+, and collagen3a1 (Col3a1)+ were classified as mesenchymal. Multiple batches were merged with the MergeSeurat function. The merged dataset was reanalyzed as above, with batch included as a latent variable in the RegressOut function. The v1 E14.5 batch 1 and batch 2 clusters were robust to the sampling differences between batches as evidenced by the contribution of cells from both batches to each cluster (
For v2 datasets (E12.5, E14.5 and E17.5), multiple canonical correlation analysis (multiCCA) from Seurat v2.3 was utilized to merge the epithelial datasets36. The top 1,000 most highly variable genes that were variable in at least 2 datasets were used for the alignment, as recommended in the Seurat tutorial. The shared correlation strength of each CC was measured with Seurat's MetageneBicorPlot, and those before the drop-off were used for alignment, analogous to the Scree plot in choosing significant PCs. We then aligned the datasets with AlignSubspace and ran an integrated t-SNE and clustering analysis, as outlined in the Seurat tutorial. Clusters were required to have 9 significantly differentially expressed genes as described above. Clusters with similar differentially expressed genes were verified with pairwise comparisons to the most related clusters (based on BuildClusterTree) and merged if they did not meet the pairwise 9-gene threshold. The Beta 2 cluster in the v2 endocrine merged time course data met the 9-gene threshold for 2 out of the 3 differential expression tests (Bimodal likelihood ratio and Wilcox rank sum tests), but had only 8 differentially expressed genes for the MAST test. Doublets were identified based on co-expression of two mutually exclusive genes, such as both mesenchymal and epithelial genes, and removed from further analysis. In the v2 datasets, rare cells (4 cells in E12.5 and 13 cells in E14.5 endocrine datasets) with high levels of hemoglobin gene expression were removed from the analysis.
Downsampling Analysis
To determine if the sequencing depth was sufficient for calling clusters, downsampling analysis was performed for the v1 E14.5 batch 1 dataset. Reads were randomly downsampled from the 10× Cellranger bam file output to a specified percentage, then grouped based on UMI to generate a count profile for each cell. The number of genes with greater than 0 counts was then calculated. UMI downsampling was performed with the SampleUMI function. A new Seurat object was created with the downsampled matrix and reanalyzed as above.
The number of UMIs/cell was downsampled from an average of 4,600 UMIs/cell in the full dataset to 200 UMIs/cell, and the median number of genes/cell and clustering robustness was then calculated. Clustering robustness was determined as the percentage of cells within the same cluster, with clusters required to maintain at least 9 genes with a 2-fold change in expression in comparison to all other clusters. Within this dataset, robust clustering was maintained all the way down to 500 UMIs/cell, when the percentage of cells in the same cluster began to climb, indicating collapsing of individual clusters. Both of these downsampling analyses indicate that sufficient sequencing depth was reached.
Pseudotemporal Ordering
We utilized Monocle 2.6.479 to order cells in “pseudotime” based on their transcriptomic similarity. For v1 time course datasets, batch-corrected values and variable genes from Seurat analysis were used as input, utilizing the gaussianff expressionFamily, and clusters were projected onto the minimum spanning tree after ordering.
For the Fev-lineage-traced dataset, UMI counts and variable genes from the Seurat analysis were used as input, utilizing the negBinom expressionFamily. To find genes differentially expressed across the branch point in the trajectory, we used monocle's internal BEAM analysis and selected genes with an FDR cutoff of 0.001. Gene expression patterns were plotted with plot_genes_branched_heatmap and plot_multiple_branches_pseudotime.
Code Availability
Scripts are available at https://github.com/sneddonucsf/2018-Developmental-single-cell-RNA-sequencing.
Data Availability
The accession number for the raw data files of the single-cell RNA sequencing analyses disclosed herein is GEO: GSE101099. The sequence data is incorporated herein by reference.
Cellular Heterogeneity in the Murine Pancreas
We first set out to characterize the major sources of cellular heterogeneity in the developing pancreas, in the most unbiased fashion possible. Two batches of mouse pancreata at E14.5, a particularly active time of expansion, morphogenesis, and diversification6 (
Identification of Novel Mesenchymal Populations
While previous studies have identified numerous markers of the various pancreatic epithelial populations6, comparatively little is known about heterogeneity among pancreatic mesenchymal cells or how they change over developmental time. We therefore turned our attention to the mesenchymal compartment by sub-clustering only mesenchymal cells (5,069 cells) and re-performing the clustering analysis (
The remaining mesenchymal clusters included proliferating mesenchymal cells (clusters 6, 7, and 8), a large cluster (cluster 10) that expressed pan-mesenchymal markers, and four clusters (clusters 2, 4, 5, and 9) each expressing a distinct signature that segregated them from cluster 10 (
Mesothelial Cells Undergo Transcriptional Changes Across Developmental Time
During organogenesis, the dynamics of each lineage are defined by the expansion, differentiation, and maturation of its constituent cells. To begin addressing how these processes change across chronological time within the developing pancreas, we performed single-cell sequencing of pancreas at two additional time points, E12.5 and E17.5 (
While the mesothelium is a well-established mesenchymal progenitor cell population for VSM and fibroblasts in multiple other organs, both the role of the mesothelium and the origin of the mesenchymal cell types within the pancreas remain uncharacterized14-17. We utilized our single-cell mesenchymal dataset to determine whether the pancreatic mesothelium may function as a mesenchymal progenitor cell population during development. We found six populations (clusters 2, 3, 4, 5, 12, and 13) that expressed VSM cell genes, such as Acta2 and Tagin, or genes known to regulate VSM development, such as Mgp18, Fhl119,20, Barx121, and Pitx222 (
To test the lineage relationships among these populations, we ordered cells in pseudotime based on their transcriptional similarity23. This analysis placed mesothelial cells on one side of the pseudotime trajectory (
Identification of a Novel Endocrine Progenitor Population
After assessing the heterogeneity within the mesenchymal compartment, we next focused on the epithelial cells. We first sub-clustered the 2,049 cells from our E14.5 dataset that comprised just the epithelial populations (
After the ductal, acinar, Ngn3+, and hormone+ populations had been accounted for, there still remained one population that eluded classification based on known marker genes. This novel population could be distinguished from all other epithelial populations by high-level expression of the E26 transformation-specific (ETS) transcription factor Fev, previously shown to be expressed within the developing pancreas but not described as a marker of a distinct population of epithelial cells24 (
Further sub-clustering of all cells within the endocrine lineage (661 cells) revealed additional sub-groups of Fev-expressing cells. The first was marked by high expression of Pax4 and Runx1 Translocation Partner 1 (Runx1t1) and lower levels of Ngn3. The second was marked by Chgb and Vimentin (Vim) (
Given that the novel Fev+ populations expressed endocrine lineage genes, we utilized pseudotime ordering23 to test the expectation that both Fev+ populations were lineage-related to the Ngn3+ progenitors that give rise to the endocrine compartment of the pancreas29. This de novo reconstruction of the developmental trajectory placed both the Fev+/Pax4+ and FevHi/Chgb+ cells between Ngn3+ endocrine progenitors and alpha and beta cells (
To validate these predicted lineage relationships, we performed an in vivo lineage trace of Ngn3+ cells. In E14.5 Ngn3-Cre; ROSA26mTmG mouse pancreata, where lineage-traced cells are membrane-GFP+31, approximately 20% of all Ngn3-lineage-traced cells were identified as the predicted FevHI population by the presence of Fev and the absence of both Ngn3 and the pan− differentiated endocrine cell marker Islet1 (Isl1) (
We next tested if the FevHi population was also present in developing human pancreatic tissue. In human fetal pancreas at 23 weeks of gestation, we observed cells that only expressed NGN3 (
We then probed hESCs undergoing directed differentiation towards the pancreatic beta cell lineage in vitro32. FEV transcript was detected in endocrine progenitor-stage cells and beta-like cells (BLCs) at levels comparable to adult human islets, but it was not detected in undifferentiated hESCs (
Endocrine Dynamics Over Developmental Time
Although we had captured comparatively fewer epithelial cells at E12.5 and E17.5 than at E14.5, we could still identify the FevHi cells at both time points (
To analyze how endocrine populations change overtime, we merged all three v2 time points into one dataset using canonical correlation analysis36. We correlated the v2 dataset to the v1 dataset and could identify all populations present in the v1 dataset (
Lineage Decisions within the Endocrine Compartment
As the in vivo lineage tracing data had revealed that the FevHi population is derived from the Ngn3+ population, we expected that the FevHi population could then function as a progenitor for the endocrine populations of the developing pancreas. We utilized a Fev-Cre; ROSA26mTmG lineage tracing strategy to label Fev-expressing cells and their progeny. We found 100% of alpha, 100% of beta, 100% of delta, 89.1% of gamma, and 23.8% of epsilon cells were Fev-lineage-traced in E14.5 pancreas (
With evidence in vivo that the majority of endocrine cells pass through a Fev-expressing stage, we next combined this lineage tracing approach with single-cell RNA sequencing to identify transcriptional regulators of endocrine differentiation. We used Fev-Cre; ROSA26mTmG pancreata to enrich for Fev-expressing cells and their progeny (membrane-GFP+) at E14.5 with FACS sorting (
We next set out to predict the lineage relationships among the endocrine cells and identify transcriptional regulators of differentiation. Pseudotime ordering identified a trajectory that began with Ngn3+ cells, transitioned into Fev+ cells, and then split into two main branches (
We next used an analysis tool in the Monocle software called branched expression analysis modeling (BEAM) to identify the genes that distinguish the paths along the two branches to either alpha or beta cells. We found gene clusters that were upregulated along different segments of the pseudotime trajectory (
To validate the predictions of our pseudotime analysis, we performed in situ hybridization for markers that defined each branch of the trajectory. First, we confirmed the expression of Peg10 and Gng12 within the FevHi population (
Materials and Methods
The materials and methods for experiments involving human cells and tissues, as disclosed in Examples 9-18 and elsewhere in the application, are presented in this Example.
Human Cells and Tissues
Expanding on the work described in preceding Examples identifying a novel pancreatic endocrine population in mice marked by the expression of the gene Fev, we have successfully verified the existence of an analogous FEV-expressing endocrine progenitor population in human cells as well. In particular, we have identified the transcriptional profile of this FEV-expressing pancreatic endocrine progenitor population in human cells. Using genomic engineering techniques (utilizing CRISPR-Cas9 editing), a number of relevant human embryonic stem cell (hESC) lines were created, including FEV-Myc, which is an hESC line in which the FEV gene has been tagged with a protein (Myc), facilitating application of ChIP-Seq technology to identify the regions of the genome to which FEV binds in pancreatic progenitor cells. A second developed cell line is FEV-GFP, which is an hESC line in which FEV expression is reported by the presence of a green fluorescent protein. The FEV-GFP line is important for isolating FEV-expressing cells from the heterogeneous culture of hESC-derived cells as they are being directed in their differentiation towards a pancreatic beta cell fate, for instance. A third cell line developed is the FEV-KO line, which is an hESC line in which the FEV gene has been deleted (knocked out). We have now performed experiments with the FEV-KO line and shown that when the gene FEV is ablated, hESC cells suffer a significant reduction in the number of pancreatic beta cells that can be made. This provides evidence that the gene itself is functionally important in this pancreatic endocrine population.
More particularly, the data disclosed herein reveals an unknown endocrine progenitor stage that is defined by high expression of Fev, a transcription factor. The data shows that all hormone-expressing endocrine lineages of the murine pancreas transit through a Fev-expressing cell stage. The data disclosed herein further establishes that similar FEV-expressing endocrine progenitor cell populations are found in human pancreatic development.
Given that FEV+ progenitors constitute a major stage in human endocrine cell differentiation, novel tools have been developed to study both the function of FEV and FEV-expressing cells in in vitro beta cell differentiation. This work forms a foundation on which improvements to in vitro beta cell differentiation can be made to more closely reflect proper endocrine cell development in vivo, thereby increasing beta cell yield at the end of this process and generating beta cells that are functional in vitro and in vivo.
The experimental data disclosed herein establish that the novel endocrine progenitor stage defined by differential expression of the transcription factor named Fev are Fev+ endocrine progenitors derived from Ngn3+ progenitors. The Fev+ endocrine progenitors give rise to hormone-expressing lineages of the murine pancreas.
This map of in vitro beta cell differentiation uncovers a novel lineage that results from mis-differentiation of FEV-expressing progenitors and opens avenues through which current in vitro beta cell differentiation methods can be improved for greater differentiation efficiency. Given our findings of Fev/FEV+ progenitors in murine development, human fetal development, and the in vitro derivation of beta cells, valuable hESC lines were engineered to study the function of the FEV gene and FEV+ endocrine progenitors during the directed differentiation of beta cells. It is expected tat FEV+ endocrine progenitor cells can also be directed to differentiate into other hormone-expressing endocrine lineages. This work resulted in a new differentiation model for human endocrine cell development and paves the way for improved in vitro pancreatic progenitor cell derivation methods that better reflect in vivo human endocrine cell development and can lead to a variety of endocrine cell types, including beta cells, alpha cells, delta cells and the like.
Human Tissue Procurement, Isolation, and Processing
Human fetal pancreata were harvested from post-mortem fetuses with approval from the ethical committee of UCSF. Tissue was obtained through two sources: the University of Washington Birth Defects Research Laboratory (12wpc_1 and 15.5wpc samples) and Advanced Bioscience Resources, Inc (12wpc_2 and 16wpc samples). Tissue was harvested at respective clinics and shipped overnight on ice in either 1×PBS (samples from University of Washington Birth Defects Research Laboratory) or RPMI media (samples from Advanced Bioscience Resources, Inc). Following delivery, tissue was washed once with 1×PBS, minced with a sterile scalpel, and dissociated in Liberase™ and 0.1 mg/mL DNase in 1×HBSS for 30-55 minutes in a 37° C. Thermomixer programmed to shake at 1000 rpm. Dissociation was quenched with 5 mM EDTA and 10% FBS in 1×HBSS. Cell suspension was filtered through a 30 μm cell strainer. Red blood cells (RBCs) were removed from the cell suspension using immunomagnetic negative selection with STEMCELL Technologies' EasySep RBC Depletion Reagent (cat. no. 18170). Following RBC depletion, cells were counted and loaded onto the 10× Chromium Platform for single-cell RNA-sequencing.
Adult human islets were isolated from cadaveric donor tissue by the UCSF Islet Production Core with approval from the UCSF ethical committee. Consented cadaver donor pancreata were provided by the nationally recognized organization UNOS via local organ procurement agencies. The identifiers were maintained at the source only, and the investigators received de-identified specimens.
Informed consent was obtained for all human (fetal and adult) tissue collection, and protocols were approved by the Human Research protection Program Committee on Human Research of UCSF.
Embryonic Stem Cell Culture and Differentiation
The hESC line HUES8 was obtained from Harvard University and used for the generation of hESC-derived beta-like cells (BLCs). Pluripotent HUES8 cells were maintained as spherical clusters in suspension in mTeSR-1 (StemCell Technologies) in 500 mL spinner flasks (Corning, VWR) on a magnetic stir plate (Dura-Mag) within a 37° C. incubator at 5% CO2, 100% humidity, and a rotation rate of 70 rpm. Cells were screened for mycoplasma contamination using the MycoProbe Mycoplasma Detection Kit (R&D Systems), according to the manufacturer's instructions.
BLCs were generated as previously described (Pagliuca et al., 2014), with additional modifications (Millman et al., 2016). In brief, HUES8 cells were seeded into a spinner flask at a concentration of 8×105 cells/mL in mTeSR1 media with 10 μM Rock inhibitor Y-27632 (STEMCELL Technologies) to allow formation of spherical clusters. Differentiation was initiated 72 hours later. Differentiation was achieved in a step-wise fashion using the following growth factors and/or small molecules: definitive endoderm (Stage 1) (1 day of 100 ng/mL Activin A (R&D Systems) and 14 μg/mL of CHIR99021 (Stemgent); 2 days of 100 ng/mL Activin A); gut tube endoderm (Stage 2) (3 days of 50 ng/mL KGF (Peprotech)); early pancreatic progenitors (Stage 3) (1 day of 200 nM LDN193189 (Fisher Scientific), 50 ng/mL KGF, 0.25 μM SANT-1 (Sigma), 2 μM Retinoic Acid (Sigma), 500 nM PdbU (EMD Biosciences), and 10 μM Rock inhibitor Y-27632 (STEMCELL Technologies); 1 day of 50 ng/mL KGF, 0.25 μM SANT-1, 2 μM Retinoic Acid, 500 nM PdbU); later pancreatic progenitors (Stage 4) (5 days of 50 ng/mL KGF, 0.25 μM SANT-1, 0.1 μM Retinoic Acid, and 10 μM Rock inhibitor Y-27632); endocrine progenitors (Stage 5) (4 days of 0.25 μM SANT-1, 0.1 μM Retinoic Acid, 1 μM XXI (EMD Millipore), 10 μM Alk5i (Axxora), 1 μM T3 (EMD Biosciences), 20 ng/mL Betacellulin (Fisher Scientific); 3 days of 25 nM Retinoic Acid, 1 μM XXI, 10 μM Alk5i, 1 μM T3, 20 ng/mL Betacellulin); BLCs (Stage 6) (6-11 days of 10 μM Alk5i; 1 μM T3). Successful differentiation was assessed at the completion of Stages 1, 3, 4, 5, and 6 via immunofluorescence or FACS for stage-specific marker genes. hESC-derived cells used for single-cell RNA-sequencing were taken at ES4 (End of Stage 4), S5D4 (Stage 5, Day 4), S5D7, S6D4, and S6D10. Cells for single-cell RNA-sequencing were dissociated with Accumax for 15-25 minutes in a 37° C. water bath. The dissociated cell suspension was neutralized with stage-specific media and filtered through a 37 μm filter. Cells were counted and then loaded onto the 10× Chromium Platform for single-cell RNA-sequencing.
In Situ Hybridization and Immunofluorescence of hESC-Derived Clusters
hESC-derived cell clusters were fixed in 4% PFA in 1×PBS for 15 minutes at room temperature (RT). Fixed clusters were washed with 1×PBS and cryoprotected overnight at 4° C. in 30% sucrose. Clusters were then embedded in OCT, and 8 μm sections were cut.
In situ hybridization was performed on 8 μm sections using RNAscope technology (Advanced Cell Diagnostics) according to the manufacturer's instructions. An in situ probe against human FEV (cat. no. 471421-C3) was used in combination with the RNAscope Multiplex Fluorescent Reagent Kit v2 for target detection. Following signal amplification of the target probes, sections were washed in 1×PBS three times and blocked in 5% normal donkey serum (NDS, Rockland Immunochemicals) in 0.1% Triton X-100 in PBS for 1 hour at RT. Tissue sections were then stained with a primary antibody against PDX1 (1:100, R&D Systems). The next day, sections were washed three times in 0.1% Tween 20 in 1×PBS and then incubated with species-specific Alexa Fluor 488-secondary antibodies (1:500, Jackson ImmunoResearch) and DAPI in 5% NDS in 0.2% PBT for 1 hour at RT. Sections were washed three times in 0.1% Tween 20 in 1×PBS, rinsed in 1×PBS, and then mounted in ProLong Gold Mounting Medium. Slides were stored at 4° C.
Images were captured on a Leica confocal laser scanning SP8 microscope. Maximum intensity z-projections were then prepared using ImageJ, where brightness, contrast, and pseudo-coloring adjustments were applied equally across all images in a given series.
Quantitative RT-PCR
hESC-derived cells at various stages of directed differentiation were collected in Trizol, and RNA was extracted with the Direct-zol RNA Miniprep kit (Zymo Research). Adult human islets were also processed this same manner for RNA extraction. Reverse transcription was performed with the Superscript IV First-Strand Synthesis System (Thermo Fisher Scientific, cat. no. 18091050) using Oligo d(T) primers and random hexamers. RT-PCR was run on an ABI Real-Time PCR System (Applied Biosystems, 384-well format) with Taqman probes for FEV (assay ID: Hs00232733_m1) and GAPDH (assay ID: Hs02758991_g1) in triplicate. Data were normalized to GAPDH.
Single-Cell Capture and Sequencing
To capture individual cells, we utilized the Chromium Single Cell 3′ Reagent Version 3 Kit (10× Genomics) (Zheng et al., 2017). Only the 15.5wpc sample was processed with the Chromium Single Cell 3′ Reagent Version 2 Kit. For all samples, 25,000 cells were loaded onto one or two wells of the 10× chip to produce Gel Bead-in-Emulsions (GEMs). GEMs underwent reverse transcription to barcode RNA before cleanup and cDNA amplification. Libraries were prepared with the Chromium Single Cell 3′ Reagent Kit. Each sample was sequenced on the NovaSeq (Illumina) in Rapid Run Mode with paired-end sequencing parameters: Read1, 98 cycles; Index1, 14 cycles; Index2, 8 cycles; and Read2, 10 cycles.
Single-Cell Analysis
CellRanger v3.0.2 software was used for all single-cell RNA-sequencing datasets with default settings for de-multiplexing, aligning reads to the human genome (10× Genomics pre-built hg38 reference genome) with STAR (Dobin et al., 2012) and counting unique molecular identifiers (UMIs) to build transcriptomic profiles of individual cells. Gene-barcode matrices were analyzed with the R package Seurat v3.0.1 (Stuart et al., 2019). We first performed a filtering step, retaining only the cells that expressed a minimum and maximum number of genes and did not exceed a specified percentage of reads that map to the mitochondrial genome. The following quality control metrics for each dataset are outlined in Table 6.
Sample name is listed along with the minimum and maximum number of genes and maximum percentage of mitochondrial genes used for quality control thresholds.
Data were then normalized with the Seurat3 function NormalizeData with default settings. This employs a global-scaling normalization that normalizes gene expression measurements for each cell by the total expression. Genes that exhibit high cell-to-cell variation were then identified using FindVariableFeatures. The highly variable genes from this analysis were then used in downstream analysis to highlight biological signal from background noise in single-cell datasets. Data then underwent linear transformation (“scaling”), which was required prior to dimensional reduction with PCA, and this scaling was done with ScaleData. PCA (Principal Component Analysis) was performed on the scaled data with RunPCA. Significant PCs (principal components) were determined with ElbowPlot, which plots principal components based on the percentage of variance exhibited by each one. These significant PCs were utilized in Seurat3's graph-based clustering algorithms, FindNeighbors and FindClusters. The resolution parameter of FindClusters was adjusted to vary the number of clusters found by the algorithm. Clusters were visualized by UMAP with Seurat3's RunUMAP and DimPlot functions. Differentially expressed genes were determined with the FindAllMarkers function. Seurat3's VInPlot, DotPlot, and FeaturePlot functions were used to visualize of expression of genes of interest across cells and clusters.
Sub-Clustering and Merging Datasets
Sub-clustering was performed by isolating clusters of interest with the Seurat3 function Subset and reanalyzing as outlined above (finding variable genes, scaling data, and identification of significant PCs). Cells were classified as endocrine based on the expression of Chromogranin A (CHGA).
Merging of all human fetal datasets was accomplished with Seurat3's Integration workflow. This integration workflow in Seurat3 identifies “anchors” across disparate single-cell datasets in order to construct harmonized references for better merging of the data and minimization of batch effect (Stuart et al., 2019). In the integration workflow, all datasets were merged into a single Seurat object and processed to the step encompassing identification of variable genes (FindVariableFeatures). Integration anchors were then identified using the FindIntegrationAnchors and used to integrate all human fetal datasets through the IntegrateData function. Following integration, data were scaled (ScaleData), significant PCs were identified (RunPCA), and UMAP-based clustering was performed (RunUMAP, FindNeighbors, FindClusters). Gene expression of specific genes were visualized by using read levels from the “RNA” slot of the integrated Seurat object (accessed by inputting “rna_gene” into gene parameter).
Pseudotemporal Ordering
For the pseudotemporal ordering analysis of the 12wpc_1 sample, we utilized Monocle v2.99.3 (named Monocle 3 alpha). Variable genes from the Seurat3 analysis of the 12wpc_1 samples (resolution 0.8) were used as input into Monocle, utilizing the VGAM::negbinomial.size expressionFamily, and clusters were projected onto the minimum spanning tree after ordering. The beginning of pseudotime was assigned using the function orderCells based on NGN3 expression.
To conduct alpha and beta branch analysis, clusters along each branch were isolated and loaded into Monocle separately. Genes that changed significantly as a function of pseudotime were identified with Monocle's differentialGenetest function, and those that displayed a q-value less than 0.001 were selected for downstream analysis. These genes were then plotted as a heatmap (using plot_pseudotime_heatmap) that clustered genes based on similarities in expression patterns along pseudotime. The expression of individual genes was plotted using Monocle's plot_genes_in_pseudotime function.
For the pseudotemporal ordering analysis of our merged human fetal and hESC-derived cell datasets, we utilized Monocle3 v0.1.0 (named Monocle 3 beta) was used. This version of Monocle3 was used because of its internal batch correction capabilities. For the merged human fetal pseudotemporal ordering analysis, variable genes from the Seurat3 integration analysis were used as input into Monocle. Clusters were projected onto the minimum spanning tree after ordering.
For the merged hESC-derived analysis, variable genes from CHGA+ sub-clustering were used as input into Monocle. To batch correct based on sample type, the residual_model_formula_str was set to “˜orig.ident” during the pre-process_cds step. To conduct branch-specific analyses, the choose_cells function was used to manually select the branches of interest in Monocle's graphical user interface. Once branches were selected, genes that changed significantly along pseudotime were identified using the graph_test function. Genes of interest were plotted along the Monocle trajectory using the plot_cells function.
Genetic Engineering of the FEV-KO hESC Line
The HUES8 hESC line was used to generate the FEV-KO line. For the FEV-KO hESC line, the FEV-KO gRNA (5′-CTGATCAACATGTACCTGCC-3′; SEQ ID NO:1) was designed on Benchling software and ordered from Dharmacon in a lyophilized format. The gRNA was suspended in nuclease-free 10 mM Tris-HCl Buffer (pH 7.4) ordered from Dharmacon (cat. no. B-006000-100) and stored as aliquots at −80° C. HUES8 hESCs were grown on Matrigel-coated tissue culture plates, and on the morning of nucleofection, media was changed to mTeSR1+10 μM Rock inhibitor Y-27632 for 2 hours prior to nucleofection. Following this incubation step, hESCs were lifted from Matrigel plates and dissociated into a single-cell suspension using TrypLE Express. Cells were incubated in TrypLE Express dissociation reagent for 6 minutes at RT. mTeSR1+10 μM Rock inhibitor Y-27632 was used to neutralize the dissociation, and cell suspension was filtered through a 37 μm filter.
To carry out the nucleofection, we mixed 2.75 μL of tracrRNA (160 uM) and 2.75 uL of the FEV-KO gRNA (160 μM) (to make the “RNA-complex”) in a PCR strip tube and incubated for 30 minutes in the 37° C. cell culture incubator. After 30 minutes, 5.5 μL of purified Cas9-NLS protein (QB3 UC Berkeley MacroLab) was added to the RNA complex, gently mixed to make the RNP (ribonucleoprotein), and incubated at 37° C. for exactly 15 minutes. After exactly 15 minutes, previously dissociated cells were resuspended in Lonza's P3 buffer from the P3 Primary Cell 4D-Nucleofector X Kit S (V4XP-3032). 10 μl of cell suspension containing 400K cells were pipetted into one well of the Lonza nucleofection strip, and 10 μl of the RNP was added. The nucleofection strip was then inserted into the Lonza 4D-Nucelofector (Lonza, AAF-1002B) and nucleofected with the CA137 setting compatible with the P3 buffer. Nucleofected cells were then transferred to a 15 mL conical with 3 mL of mTeSR1 containing 10 μM Rock inhibitor Y-27632 and pen/strep (penicillin/streptomycin). Cell viability was determined via Moxiflow, and cells were plated in one well of a 6-well plate coated with Matrigel. Cells were grown for 2-3 passages in mTeSR1 containing 10 μM Rock inhibitor Y-27632 and pen/strep to allow for recovery from nucleofection.
To determine genomic editing efficiency of the FEV-KO nucleofection experiment, genomic DNA from nucleofected cells was harvested in QuickExtract DNA Extraction (Lucigen, QE09050) and then used for PCR amplification. The following forward and reverse primers targeting the FEV-KO editing site were used to produce a 491-bp amplicon: 5′-CCGTCTTCTCCTCCTTGTCACC-3′ (SEQ ID NO:2) and 5′-CTCGGCCACAGAGTACTCCAC-3′ (SEQ ID NO:3). This amplicon is GC-rich, requiring use of a PCR polymerase capable of handling GC-rich amplicons (PrimeSTAR GXL Premix, Clontech). This DNA amplicon and a wild-type DNA amplicon were sent to Quintarabio for Sanger sequencing. The chromatographs of each sequencing run were used for TIDE (Tracking of Indels by Decomposition) analysis, which estimates the frequency of insertions and deletions (indels) in a pool of cells that has undergone genomic editing (Brinkman et al., 2014). Cutting efficiency of hESCs nucleofected with FEV-KO gRNA was then determined.
To derive a clonal FEV-KO line from this heterogeneous pool of hESCs that have no mutation in the FEV locus, a mutation(s) in one FEV allele, or mutations on both FEV alleles, these cells were clonally plated on Matrigel-coated plates. Approximately 1,500 cells were dispersed onto a 10 cm Matrigel-coated plate and allowed to grow for 9-10 days in mTeSR1. For the first 4-5 days of culture, cells were cultured in mTeSR1 containing 10 μM Rock inhibitor Y-27632. Clonal colonies were then hand-picked under a colony-picking microscope under sterile conditions. These hand-picked colonies were each transferred into one well of a 96-well plate, allowed to grow for 2-3 days, and then successively passaged into large-plate formats (96-well to 24-well to 6-well to 10 cm dish). Clonality was first determined through TIDE analysis, as outlined above, and confirmed with TOPO cloning of the FEV-KO PCR amplicon.
Genetic Engineering of the FEV-KI hESC Lines
The HUES8 hESC line was used to generate the FEV-MYC, FEV-GFP, and FEV-tNGFR lines. The MYC, GFP, and tNGFR inserts were all commercially synthesized as gene blocks from Integrated DNA Technologies. 5′ and 3′ FEV locus homology arms that were 400 bp in length were then added to each of the MYC, GFP, and tNGFR gene blocks using In-Fusion HD Cloning (Clontech, 638920). These homology arms flanked the cut site targeted by the FEV-KI gRNA. The result of In-Fusion HD cloning was a pUC19 plasmid containing a MYC, GFP, or tNGFR insert flanked by 5′ and 3′ FEV homology arms. These plasmids were transformed into Stellar Competent Cell (Clontech, 636766), and PCR amplification off of these isolated plasmids generated a PCR amplicon for use as our targeting template to knock in MYC, GFP, and tNGFR into the FEV locus. The following forward and reverse primers were used in PCR to generate each targeting template from each plasmid: 5′-TGAACTACGACAAGCTGAGCCG-3′ (SEQ ID NO:4) and 5′-TCCTTGGGGAAGAGCAAAAGTG-3′ (SEQ ID NO:5).
For knock-in of MYC, GFP, and tNGFR into the FEV locus, a FEV-KI gRNA (GCCATTACCACTAGACGGGG; SEQ ID NO:6) was designed using Benchling software and targeted the end of exon 3 of the FEV locus. This FEV-KI gRNA cut immediately preceding the FEV stop codon found at the end of exon 3 and would facilitate the knock-in of each insert in-frame with the FEV locus. On the morning of nucleofection, HUES8 hESCs were fed with mTeSR1+10 μM Rock inhibitor Y-27632 for 2 hours. Following this incubation step, hESCs were lifted from Matrigel-coated plates and dissociated into a single-cell suspension using TrypLE Express. Cells were incubated in TrypLE Express dissociation reagent for 6 minutes at RT. mTeSR1+10 μM Rock inhibitor Y-27632 was used to neutralize the dissociation, and cell suspension was filtered through a 37 μm filter.
To carry out the nucleofection, 1.25 μL of tracrRNA (160 uM), 1.25 μl of FEV-KI gRNA (160 μM), and 1 μg of either the MYC, GFP, or tNGFR targeting templates were mixed in a PCR strip tube and incubated for 30 minutes in a 37° C. cell culture incubator. After 30 minutes, 2.5 μL of purified Cas9-NLS protein (QB3 UC Berkeley MacroLab) was added, gently mixed to make the RNP complex, and incubated at 37° C. for exactly 15 minutes. Dissociated cells were pelleted at 1000 rpm for 3 minutes and resuspended in Lonza P3 buffer (Lonza, V4XP-3032). 10 μL of cell suspension containing 400,000 cells were then pipetted into a Lonza cuvette, and 10 μL of the RNP complex+targeting template was added. The cuvette was then inserted into the Lonza 4D-Nucelofector (Lonza, AAF-1002B) and nucleofected with the CA137 setting compatible with the P3 buffer. Nucleofected cells were then transferred to a 15 mL conical tube with 3 mL of mTESR containing 10 μM Rock inhibitor Y-27632 and pen/strep. Cell viability was determined via Moxiflow, and cells were plated in one well of a 6-well plate coated in Matrigel. Cells were grown for 2 passages to allow for recovery from nucleofection.
To determine if the MYC, GFP, and tNGFR inserts were successfully knocked-in, genomic DNA from nucleofected cells was harvested in QuickExtract DNA Extraction (Lucigen, QE09050) and used for PCR amplification. The following forward and reverse primers were used: MYC: 5′-AGATCCAGCTGTGGCAGTTTCT-3′ (SEQ ID NO:7) and 5′-ACCAGACAAGGATTGAGGGAGC-3′ (SEQ ID NO:8) GFP: 5′-CGTGCATCTGGAAAGCTACGTG-3′ (SEQ ID NO:9) and 5′-CTTGAAGAAGTCGTGGCGCTTC-3′ (SEQ ID NO:10) tNGFR: 5′-TGAACTACGACAAGCTGAGCCG-3′ (SEQ ID NO:4) and 5′-TCCTTGGGGAAGAGCAAAAGTG-3′ (SEQ ID NO:5). Presence of a knock-in band that was larger than the FEV wild-type band was indicative that a subset of nucleofected cells carried the insert.
To derive clonal FEV-KI lines from a heterogeneous pool of hESCs that either had the knock-in insert or not, cells were clonally plated on Matrigel-coated plates. Approximately 1,500 cells were dispersed onto a 10 cm Matrigel plate and allowed to grow for 9-10 days in mTeSR1. For the first 4-5 days of culture, cells were cultured in mTeSR1 containing 10 μM Rock inhibitor Y-27632. These hand-picked colonies were each transferred into one well of a 96-well plate, allowed to grow for 2-3 days, and then successively passaged into large-plate formats (96-well to 24-well to 6-well to 10 cm dish). Genomic DNA was isolated from each clonal line and the insert was confirmed through PCR using same primers as indicated above. Sanger sequencing of the genomic FEV locus confirmed that the MYC, GFP, and tNGFR had no mutations and were in-frame with the endogenous FEV locus.
Generation of Gene KOs During Directed Differentiation of hESCs
Approximately 100-150×106 End Stage 4 (ES4) cells from the directed differentiation protocol were dissociated in Accumax for 15-25 minutes in a 37° C. water bath. The dissociated cell suspension was passed through a 37 μm filter. Cell count and viability were determined with a Moxiflow cell counter. Cells were pelleted at 1000 rpm for 3 minutes and kept in ES4 media until nucleofection.
For nucleofection, the large format of Lonza's nucleofection kits (P3 Primary Cell 4D-Nucleofector X Kit L, V4XP-3024) was used, which accommodates nucleofection of up to 20×106 cells per nucleofection vessel. Four conditions were typically included in these experiments: non-nucleofected control, scramble control, hAAVS1 control, and KO of gene of interest. The non-nucleofected control group contained ES4 cells that did not go through nucleofection. The scramble control group contained ES4 cells that were nucleofected with a scramble gRNA (GGTTCTTGACTACCGTAATT; SEQ ID NO:11) that is not predicted to cut anywhere in the human genome. The hAAVS1 control included ES4 cells that were nucleofected with a gRNA targeting a safe harbor locus in the human genome AAVS1 (GGGGCCACTAGGGACAGGAT; SEQ ID NO:12. The KO of gene of interest group contained ES4 cells that were nucleofected with a gRNA targeting the gene of interest we wished to knock out. All gRNAs were ordered from Dharmacon.
For each nucleofection set of 10-20×106 ES4 cells, 9.5 μL of tracrRNA (160 μM), 9.5 μL of the FEV-KI gRNA (160 μM) were mixed in a PCR tube and incubated for 30 minutes in a 37° C. cell culture incubator. After 30 minutes, 19 μL of purified Cas9-NLS protein (QB3 UC Berkeley MacroLab) was added, gently mixed to make the RNP complex, and incubated at 37° C. for exactly 15 minutes. Dissociated ES4 cells were pelleted at 1000 rpm for 3 minutes, and each set of 10-20×106 ES4 cells were resuspended in 64 μL of Lonza P3 buffer (from V4XP-3024). Each set of cells were then pipetted into a large Lonza nucleofection vessel, and 36 μl of the RNP were added.
Nucleofection vessel was then inserted into the Lonza 4D-Nucleofector (Lonza, AAF-1002B) and nucleofected with the CA137 setting compatible with the P3 buffer. Nucleofected cells were then transferred to a 15 mL conical tube with 10 mL of S5D1 media. Cell viability was determined via Moxiflow.
Following nucleofection, cells were immediately re-aggregated into clusters using AggreWell 400 plates (STEMCELL Technologies, 34415). Wells in the AggreWell 400 plates were washed with an Anti-Adherence Rinsing Solutions (STEMCELL Technologies, 07010) and centrifuged in a swinging bucket rotor at 1300×g for 5 minutes. Rinsing solution was removed, and S5D1 media was used to rinse wells. S5D1 media was aspirated, and 1.2×106 cells were then pipetted into each well of an AggreWell 400 plate. Plates were spun at 100×g for 3 minutes to facilitate re-aggregation of cells in each microwell and then were observed under microscope to verify even distribution of cells among microwells. Plates were placed in the 37° C. cell culture incubator, and spheroids formed by 48 hours (by S5D3). On S5D3, clusters were removed from the AggreWell plates and cultured in either miniature spinner flasks called Biotts (BWV-503A) set at a 70 rpm rotation speed or in 6-well ultra low-attachment plates (5 mL of media with approximately 5×106 cells per well) placed on an orbital shaker set to 100 rpm. Directed differentiation of these nucleofected clusters was continued either in Biotts or in a 6-well ultra low-attachment plate.
FACS of hESC-Derived Cells
BLC clusters were dissociated in Accumax for 15-25 minutes in a 37° C. water bath. The dissociated cell suspension was passed through a 37 μm filter. Cells were pelleted at 1000 rpm for 3 minutes and fixed in 4% PFA for 12 minutes at RT. Cells were washed in 1×PBS, pelleted again, and resuspended in 1×PBS. Fixed cells were stored at 4° C. prior to staining for FACS.
For FACS staining, cells were permeabilized using 1× Permeabilization Buffer (Invitrogen, 00-8333-56) for 5 minutes at RT. Cells were then incubated in primary antibody diluted in Blocking reagent (0.2% Triton X-100, 5% NDS, 1% Bovine Serum Albumin (BSA) in 1×PBS) overnight at 4° C. Primary antibodies used were anti-Chromogranin A (1:500, Abcam ab15160) and anti-C-Peptide (1:200, EMD Millipore 05-1109). The next day, cells were washed in 1× Permeabilization Buffer for 5 minutes at RT and incubated in species-specific Alexa Fluor 488- and 555-conjugated secondary antibodies (1:500, Jackson ImmunoResearch) for 30 minutes at RT. Cells were then washed in 1× Permeabilization Buffer, pelleted, resuspended in 1×PBS, and analyzed with BD Fortessa Analyzer.
Diversity of Cell Types in the Developing Human Pancreas
Improving the ability to generate terminally differentiated cell types in all animals, including humans, will be beneficial in providing higher quality healthcare, at lower cost, and also in improving the quality of life for many. Gaining a better understanding of the cell stages required for human endocrine cell development as well as the transcriptional circuitry driving lineage allocation into distinct endocrine linages will refine our ability to generate these hormone-expressing cell types from human embryonic stem cells.
The discovery of a novel endocrine progenitor defined by high Fev expression in mouse, as disclosed herein, prompted the question of whether additional endocrine progenitor stages exist in human endocrine cell development beyond the NGN3+ endocrine progenitor stage. Defining these stages in humans can be leveraged to more properly mimic human endocrine cell development in vitro during directed differentiation protocols that harness the power of hESCs.
The different cellular compartments and their transcriptional profiles from human fetal pancreas were characterized. The focus was on a 12wpc time point, which represents a period of peak NGN3 expression and active cell differentiation in the developing human pancreas (Nair and Hebrok, 2015). Tissue from this 12wpc time point was dissociated into a single-cell suspension, and RBCs were removed via immunomagnetic separation. The resulting single-cell suspension was loaded onto two wells of the 10× Chromium Single-Cell Platform and prepared for sequencing using version 3 (V3) chemistry. Following sequencing and de-multiplexing of single-cell data, UMAP-based clustering of merged well replicates revealed 22 cell clusters organized into acinar, ductal, endocrine, mesenchymal, endothelial, immune, and nerve populations based on the expression of known marker genes, such as CPA1 (acinar), SOX9 (ductal), CHGA (endocrine), COL1A1 (mesenchymal), PECAM1 (endothelial), PTPRC (immune), and SOX10 (nerves) (
Identification of Novel Cell Stages During Human Endocrine Cell Development
Given our previous identification of novel progenitor stages in mouse pancreatic development, we next focused on the endocrine compartment of the developing human pancreas in order to determine if additional endocrine stages exist beyond those characterized by NGN3+ progenitors and differentiated hormone+ endocrine cells. Sub-clustering of the CHGA+ cell clusters resulted in increased resolution of the endocrine lineage populations present in 12wpc human fetal pancreas, revealing 11 distinct endocrine lineage populations (
Three additional cell clusters (clusters 6, 8, and 9) that were devoid of any hormone expression (
The inclusion of other hormone-expressing endocrine lineages in pseudotemporal ordering did not result in a continuous differentiation trajectory (
Pairwise comparisons and examination of the top differentially expressed genes of clusters 6, 8, and 9 revealed that these populations represent novel cell stages of human endocrine cell differentiation at a resolution that we have not been able to appreciate with previous techniques. NGN3 expression was concentrated within the common endocrine progenitor population (cluster 6), although NGN3 was not among the top 5 differentially expressed genes (
Candidate Lineage Regulators of the Beta Cell Lineage
The onset of NGN3 expression marks the beginning of endocrine cell development as cells differentiate towards a hormone+ endocrine lineage. However, the transcriptional programs that guide these endocrine progenitors toward a distinct hormone-expressing endocrine lineage are not well defined in human endocrine cell differentiation. With the lack of lineage tracing tools available for in vivo human studies, single-cell RNA-sequencing data was used to make inferences about the transcriptional machinery that regulates endocrine lineage allocation. Given that we observed distinct stages of cellular differentiation leading to both alpha and beta lineages (
Pseudotemporal ordering was used to identify genes that were differentially expressed across a single-cell trajectory. This analysis was first applied to the beta lineage branch, which exhibited differentiation starting with cluster 6 cells (common endocrine progenitors) to cluster 8 cells (pre-beta progenitors) and finally to beta cells (
In view of the data, it was expected that genes within gene clusters 1, 6, and 7, which were upregulated during the pre-beta progenitor stage, would serve as regulators of beta cell lineage allocation. FEV was found in gene cluster 7. Genes that displayed high upregulation during the pre-beta progenitor stage also included genes known to be involved in beta cell differentiation and function, such as CHGB, SCG5, ERO1B, MAFB, and PAX6 (
We also identified candidate regulators not previously known to be involved in beta cell lineage allocation. These genes were organized into three broad categories: those that were imprinted, those involved in neural development, and others involved in transcription and canonical signaling pathways. Imprinted genes that were upregulated during beta cell differentiation included DLK1, MEG3, GNAS, PLAGL1, PEG3, and PEG10 (
Upregulated genes known to be involved in neural development included ASCL2, AHI1, and SEZ6L2 (
Candidate Lineage Regulators of the Alpha Cell Lineage
A similar analysis was applied to identify genes that were differentially expressed during differentiation into alpha cells. Based on pseudotemporal ordering, differentiation of the alpha lineage from endocrine progenitors began with cluster 6 (common progenitors) that differentiated into cluster 9 cells (pre-alpha progenitors), which then became differentiated alpha cells found in cluster 9 (
Given that gene clusters 4 and 5 contained genes that were upregulated specifically after NGN3 downregulation and the acquisition of alpha cell identity, these genes were expected to be regulators of alpha lineage allocation. Similar to the analysis of the beta cell lineage, MAFB, PAX6, ERO1B, AHI1, PEG10, SCG5, and ACVR1C were found to be upregulated along alpha cell differentiation, indicating that these markers are common genes upregulated during endocrine cell differentiation as a whole. We observed upregulation of various neural transcription factors and genes during alpha cell differentiation. BEX2, BEX4, and BEX5 were all upregulated during alpha cell fate allocation and are members of the brain-expressed X-linked transcription factor family that are highly expressed in the brain (Alvarez et al., 2005) (
Cellular and Transcriptional Dynamics of the Developing Endocrine Compartment
To understand endocrine cell development across actual developmental time, single-cell RNA-sequencing was performed on tissues at three additional time points to add to the analysis on 12wpc human fetal pancreas (referred to as 12wpc_1): a second biological replicate of 12wpc (referred to as 12wpc_2), 15.5wpc, and 16wpc. All datasets were depleted of red blood cells through immunomagnetic separation and were generated using the 10× Genomics version 3 (V3) sequencing chemistry, except for the 15.5wpc sample, which was enriched for EPCAM+ cells through FACS and processed using version 2 (V2) sequencing chemistry. All datasets were merged with Seurat 3's new integration method for merging and batch correction, resulting in 31 distinct clusters (
Sub-clustering of the endocrine lineage resulted in 15 distinct populations (
We next sought to reconstruct lineage relationships across multiple time points through pseudotemporal ordering. Batch effect, unfortunately, is a major issue that still confounds single-cell RNA-sequencing analysis, despite multiple groups developing algorithms to address this problem (Butler et al., 2018; Haghverdi et al., 2018; Stuart et al., 2019). In our merged dataset, batch effect became problematic as our 15.5wpc sample was processed with V2 10× Genomics version chemistry as opposed to V3 chemistry, which was utilized for processing the 12wpc and 16wpc samples. V3 chemistry increased the sensitivity of gene capture, and this was particularly evident by the percentage of mitochondrial genes captured in V3 datasets, in which more mitochondrial genes were represented (
Understanding the Emergence of Distinct Cellular Compartments During In Vitro Beta Cell Differentiation at Single-Cell Resolution
Directed differentiation of hESCs to a beta cell lineage represents a powerful approach for not only generating beta cells for diabetes but also understanding human beta cell differentiation. Given the significant heterogeneity in cells generated by directed differentiation of hESCs towards the beta lineage, single-cell RNA-sequencing was leveraged to classify distinct cellular populations that arose across five main stages of in vitro beta cell differentiation: stages containing early-, middle-, and late-stage endocrine progenitors (ES4, S5D4, and S5D7) and two stages within the beta lineage stage (S6D4 and S6D10). UMAP-based clustering of all five time points revealed the presence of PDX1+ clusters, reflecting induction towards the pancreatic lineage during the directed differentiation towards the beta lineage (
hESC-Derived FEV+ Cells are Transcriptionally Similar to In Vivo FEV+ Progenitors
Given that we had identified an endocrine progenitor stage defined by FEV expression in both mouse (Byrnes et al., 2018) and human fetal beta cell development (
Mapping In Vitro Beta Cell Differentiation at Single-Cell Resolution
The lineage relationships among hESC-derived endocrine cells during in vitro beta cell differentiation were also reconstructed. First, all CHGA+ endocrine clusters from each sampled time point were merged using Seurat 3 (
FEV Appears to be Required for Proper Human Beta Cell Differentiation and Function
In addition to identifying FEV as a marker for endocrine progenitor stages in human endocrine cell development, we wanted to determine if FEV had any functional role in beta cell differentiation. In Fev knockout (KO) mice, glucose clearance from the blood following a glucose challenge was significantly slowed, and the insulin content of beta cells was decreased (Ohta et al., 2011). Given that this study utilized a whole-body Fev KO, the defects in glucose homeostasis and the reduction in insulin content could have been a result of a requirement for FEV in non-pancreas cells or for FEV function in multiple stages in the lifetime of a beta cell. To test the requirement of FEV in human beta cell differentiation and function, the in vitro beta cell differentiation platform was used to first generate a FEV-KO hESC line through CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9-mediated genomic editing (
Generation of New Tools and Platforms for Understanding Human Beta Cell Development: Identification of FEV Transcriptional Targets, Isolation of FEV-Expressing Cells During In Vitro Beta Cell Differentiation, and Validation of Novel Candidate Regulators of Beta Cell Lineage Allocation and Function
The discovery that the transcription factor FEV is expressed in hESC-derived endocrine progenitors and beta-like cells prompted us to generate tools through which we could interrogate the function of the FEV gene. Through CRISPR/Cas9-mediated genomic editing, a FEV-MYC hESC line was constructed in which a MYC epitope tag was fused to the endogenous FEV transcription factor at the C-terminus (
Given that FEV is a transcription factor that is required for proper endocrine differentiation in an in vitro beta cell differentiation platform, the generation of this FEV-MYC hESC line is expected to be valuable in interrogating the mechanism through which FEV regulates proper human beta cell differentiation. ChIP-seq on FEV+ endocrine progenitors at Stage 5 of our in vitro differentiation can identify transcriptional targets of FEV (
As disclosed herein, tools to isolate and characterize the FEV-expressing cell population during human beta cell differentiation have been developed. Two FEV reporter hESC lines have been constructed: a FEV-GFP line and a FEV-tNGFR (truncated Nerve Growth Factor Receptor) line (
Purification of FEV-expressing cells at defined stages of the in vitro beta cell differentiation process will enable us to understand the differences among FEV-expressing populations at each differentiation stage. Specifically, purifying FEV+ endocrine progenitors at stage 5 of our differentiation program will permit small molecule screens to identify compounds that can either induce progenitor expansion prior to beta cell lineage commitment or enhance differentiation toward the beta lineage (
The in vivo and in vitro single-cell RNA-sequencing analyses have resulted in the identification of candidate beta cell lineage regulators. In order to functionally validate these candidate regulators, we developed a flexible platform on which we can test whether these genes regulate beta cell lineage allocation (
The preceding Examples establish a platform and methodologies useful in promoting and optimizing protocols for directing progenitor cells into a differentiation pathway leading to mature, functional alpha and beta cells. The present disclosure contains several discrete advances useful in achieving these goals.
Redefining the NGN3+ Endocrine Progenitor Population in Human Pancreatic Development
In human endocrine cell development, NGN3 has long been thought to mark the endocrine progenitor population, given the function of Ngn3 in mouse pancreatic development. Indeed, NGN3 is required for endocrine cell differentiation in human endocrine cell development, as inactivating mutations of NGN3 lead to neonatal diabetes (Pinney et al., 2011; Wang et al., 2006). Beta cell mass is suspected to be reduced, not absent, in human cases of inactivating NGN3 mutations given that C-peptide is detected in the blood, albeit at low levels (Pinney et al., 2011). This is in contrast to mouse development, in which NGN3 ablation halts beta cell generation altogether (Gradwohl et al., 2000). Through the studies of human endocrine cell development disclosed herein, NGN3 did not appear to be the most robust marker of the endocrine progenitor population common to hormone-expressing lineages, such as the alpha and beta lineages, in our 12wpc_1 human fetal pancreas dataset. Other markers that appeared to more faithfully label this common endocrine progenitor population included EMC10, SOX4, HES6, and KRT19. CTD-2545M3.8 also emerged from our differential gene expression analysis as a marker specific to this common endocrine progenitor population, but awaits functional characterization.
In murine endocrine development, Ngn3+ endocrine progenitors give rise to all five hormone-expressing lineages of the pancreas (Gradwohl et al., 2000; Heller et al., 2005). However, in the human fetal pancreas, the NGN3-expressing common endocrine progenitor population appeared to only give rise to alpha and beta lineages. In our pseudotemporal ordering analysis, there was no trajectory that connected NGN3-expressing progenitors to the SST-expressing delta lineage or the GHRL-expressing epsilon lineages. A distinct PPY-expressing gamma population was not observed, as all PPY-expressing cells also expressed GCG and thus were annotated as alpha cells. If the difference in differentiation potential between human and mouse cells reflects true lineage relationships of NGN3-expressing endocrine progenitors in the developing human pancreas, this would depart from the dogma established by findings of the lineage potential of Ngn3+ progenitors in murine pancreatic development.
Identification of Novel Pre-Alpha and Pre-Beta Cell Stages in Human Pancreatic Development
Mapping endocrine cell development at a higher resolution using single-cell RNA-sequencing can be leveraged for developing new methods to generate endocrine cell types more efficiently from stem cell sources. Disclosed herein is the identification of pre-alpha and pre-beta progenitor stages that provide increased resolution regarding the steps required to differentiate into alpha or beta lineages in human pancreatic development. The work disclosed herein on human fetal pancreatic development offers novel endocrine progenitor stages onto which we can compare and contrast the biological relevance of the murine progenitor stages to those of human. The advent of single-cell RNA-sequencing has led to the discovery of several endocrine progenitor stages in mouse pancreatic development. One example is the work disclosed herein, which reveals an intermediate endocrine progenitor population defined by high Fev expression. Fev expression has also been identified in endocrine progenitor populations reported by several other single-cell RNA-sequencing studies of murine pancreatic development (Krentz et al., 2018; Scavuzzo et al., 2018), confirming the reproducibility of our finding. This Fev+ endocrine progenitor is derived from a Ngn3+ population, and differentiated endocrine lineages in the murine pancreas transit through a Fev− expressing cell stage (Byrnes et al., 2018).
Within the Fev+ progenitor population, cells that appeared to be pre-specified towards an alpha or beta cell fate were found. This is analogous to human pancreatic development in which we not only identified endocrine progenitors that expressed FEV but also observed that these FEV-expressing progenitors appeared to be already lineage-specified towards an alpha or beta cell fate. The in silico reconstruction of endocrine lineage relationships indicated that endocrine cell fate decisions in progenitors occurs at the Fev/FEV-expressing cell stage in both mouse and human.
Beyond this Fev-expressing endocrine progenitor stage, there are additional endocrine progenitor stages that have been identified in murine development. In particular, four distinct endocrine progenitor stages (termed EP1-4) have been proposed in mouse endocrine cell development (Yu et al., 2019). Expression of Ngn3, the canonical pro-endocrine lineage marker in pancreatic development, increased in EP1, peaked in EP2, decreased in EP3, and was not observed in EP4. Expression of Fev was found in EP3 and EP4 stages only (Yu et al., 2019), which is concordant with Fev being downstream of Ngn3 (Byrnes et al., 2018; Miyatsuka et al., 2014). Interestingly, many of the differentially expressed genes in each EP stage were also identified as top differentially expressed genes in either our human endocrine progenitor clusters or during pseudotemporal ordering. Specifically, Krt19 and Gadd45a, two genes that defined a human common endocrine progenitor stage in our dataset of 12wpc_1 human fetal pancreas, were found to be differentially expressed in EP2. Several candidate beta lineage regulators in human fetal development were also found in EP1 (Arid5b), EP3 (Ahi1), and EP4 (Rbp4, Peg10, Acvr1c, Sez6l2) (Yu et al., 2019). Similarly, several candidate alpha lineage regulators in human fetal development were found in EP3 and EP4 (Arx, Irx2, Fam46a, Slc30a8, Slc7a2, Slc7a8, Cryba2, St18, and Alcam) (Yu et al., 2019). Thus, these EP stages found in murine endocrine development appear to also have relevance to endocrine progenitor stages found in human fetal pancreatic development.
Transcriptional Mechanisms Underlying Fate Decisions are Shared Across Tissues
The single-cell RNA-sequencing analysis of human endocrine lineage allocation identified many candidate regulators previously identified and studied in the nervous system. Despite their derivation from different germ layers, both the pancreatic endocrine and neural lineages employ many of the same transcription factors that regulate their own development, including Ngn3, NeuroD1, Nkx2.2, Nkx6.1, Pax family of transcriptional regulators, and Fev (Blake and Ziman, 2014; Churchill et al., 2017; Gradwohl et al., 2000; Hendricks et al., 1999; Mastracci et al., 2013; Napolitano et al., 2015; Ohta et al., 2011; Pataskar et al., 2016; Prakash et al., 2009; Qi et al., 2001; Schaffer et al., 2010; Simon-Areces et al., 2010; St-Onge et al., 1997). These transcriptional similarities have an evolutionary basis, as the main source of insulin in invertebrates is in neurons (Wong et al., 2014). Thus, from an evolutionary perspective, it is not surprising that additional genes previously identified to be required for proper nervous system development and function are also implicated in pancreatic endocrine development and, more specifically, lineage allocation.
The development of enteroendocrine cells (EEs) in the intestine also shares striking similarity to pancreatic endocrine cell development. Proper differentiation of EEs in the intestine during development requires transcription factors also critical for pancreatic endocrine cell differentiation, including Ngn3 (Jenny et al., 2002; López-Díaz et al., 2007; Schonhoff et al., 2004), Nkx2.2 (Gross et al., 2016), Isl1 (Terry et al., 2014), NeuroD1 (Naya et al., 1997), Pax4 (Beucher et al., 2012a). As in pancreatic endocrine cell development, the EE lineage comprises multiple hormone-expressing cell types that are derived from a common progenitor cell defined by Ngn3 (Jenny et al., 2002). Recent work applying single-cell RNA-sequencing to murine EE development uncovered novel markers and lineage-specific regulators of the multiple EE lineages (Gehart et al., 2019), and many of these genes overlapped with the markers and candidate transcriptional regulators that we identified in mouse and human endocrine cell development and lineage allocation. Ngn3+ EE progenitors differentially express Sox4, Tox3, and Gadd45a (Gehart et al., 2019), all of which were also defining markers of our common endocrine progenitor in human endocrine cell development. Known hormone-specific lineage regulators in pancreatic endocrine cell development, such as Arx, Pax6, and Isl1, were also identified as EE-specific lineage regulators (Gehart et al., 2019). Interestingly, a number of novel candidate lineage regulators that we identified in mouse and human endocrine lineage allocation were also found to be lineage-specific regulators of the different EE lineages (Gehart et al., 2019). These include Nr4a2, Smarca1, Peg3, In, S100a1, and Klf4 (Gehart et al., 2019).
Timing of Endocrine Lineage Fate Decisions
The timing of endocrine lineage fate commitment in pancreatic development is not fully understood. The Ngn3+ endocrine progenitor stage has long been regarded as the master stage prior to endocrine cell differentiation, but the single-cell RNA-sequencing studies of pancreatic development disclosed herein have identified additional progenitor stages that arise between initial Ngn3 expression and acquisition of differentiated cell identity. This increased resolution of endocrine cell differentiation has provided us with new cell stages that we can interrogate for determining when endocrine lineage decisions are made. From both the mouse and human studies of endocrine cell development disclosed herein, Fev/FEV-expressing endocrine progenitors were already specified towards an alpha or beta cell fate. This heterogeneity in Fev/FEV-expressing progenitors suggests that endocrine lineage specification occurs at or before this Fev/FEV-expressing progenitor stage. The single-cell RNA-sequencing combined with pseudotemporal ordering identified endocrine progenitor populations that appeared to be fated towards one specific endocrine lineage.
The timing of endocrine fate decisions can also be regulated by extrinsic signals derived from the surrounding microenvironment. In murine development, the developmental time at which Ngn3+ progenitors form corresponds to their ultimate hormone lineage selection (Johansson et al., 2007). The competence window for alpha differentiation occurs earliest in murine pancreatic development, resulting in alpha cells being the first emerging endocrine lineage, followed by beta and gamma cells, and then lastly followed by delta cells (Johansson et al., 2007). In contrast, in human pancreatic development, the beta lineage is the earliest endocrine cell type to be detected (at 6wpc), followed by alpha cells (at 8-9wpc), delta cells (10wpc), and gamma cells (at 17wpc) (Jeon et al., 2009; Piper et al., 2004). Without wishing to be bound by theory, the differences in timing of emergence of endocrine lineages between mouse and human could be a direct result of the changing microenvironment during development that can be providing dynamic cues that promote one endocrine lineage over the other. From murine studies, we know that several compartments of the microenvironment influence pancreatic development, including vasculature, nerves, and mesenchyme (Borden et al., 2013; Golosow and Grobstein, 1962; Landsman et al., 2011; Magenheim et al., 2011; Reinert et al., 2013). However, the cellular composition of each microenvironment compartment can widely differ between that of mouse and human. From the single-cell profiling of human fetal pancreas provided herein, we identified several populations of endothelial cells whose transcriptional expression profiles changed throughout the course of development. These changes may influence the competency of endocrine progenitors to differentiate into distinct hormone lineages, either through secreted signaling molecules or direct interactions. Our single-cell profiling in both mouse and human pancreatic development also reflects different mesenchymal and nerve populations whose dynamics may regulate endocrine differentiation at distinct periods in development.
FEV in Human Endocrine Cell Differentiation and Function
Disclosed herein is an investigation of the role of FEV in human endocrine cell differentiation and function, which highlights potential differences between Fev/FEV in mouse versus human. In both mouse and human endocrine cell development, Fev/FEV was expressed in an intermediate progenitor stage that followed initial NGN3 expression and preceded hormone acquisition (Byrnes et al., 2018). FEV was also expressed in endocrine progenitor stage cells during in vitro beta cell differentiation from hESCs, which was concordant with FEV expression in endocrine progenitors in human fetal pancreatic development. Notably, although Fev-KO mice do not exhibit obvious differentiation defects in the islet lineages during development, we did observe a reduction in the differentiation into CHGA+/CPEP+ beta cells in the in vitro beta cell differentiation model. This indicates that FEV is required for human beta cell differentiation and is dispensable for mouse beta cell differentiation. Notable differences were also observed between Fev/FEV in mouse and human differentiated endocrine cells. While Fev expression persists in the alpha and beta lineages during mouse pancreatic development, FEV expression was downregulated in beta cells and only maintained in the alpha lineage in human pancreatic development. Single-cell RNA-sequencing of adult human islets has indicated that FEV is expressed in alpha cells and not beta cells (Segerstolpe et al., 2016). This is in contrast to the in vitro beta cell differentiation system, in which beta cells maintained FEV expression following differentiation of the FEV+ endocrine progenitor stage. In mouse beta cells, FEV binds to the insulin promoter to regulate Insulin transcription and, thus, insulin production. Given that FEV turns off in differentiated human beta cells in vivo, it is possible that FEV is either not needed for beta cell function or FEV inhibits beta cell function. In the in vitro beta cell differentiation system, we observed a subset of INS+ beta cells that did not express FEV, whereas another subset of INS+ beta cells did express FEV. The INS+/FEV− hESC-derived beta cells may correspond to bona fide beta cells found in vivo, and the INS+/FEV+ hESC-derived beta cells may either be mis-differentiated or on their way towards a FEV− state.
Identification of FEV transcriptional targets provides a clearer picture of its function. In the human beta cell lineage, loss of FEV coincided with a reduction in beta cell differentiation. Given that FEV was expressed in pre-beta progenitors in vivo and hESC-derived endocrine progenitor stage cells, FEV is expected to serve as a key transcriptional regulator for differentiation from a progenitor to a beta cell. Using the FEV-MYC hESC line during in vitro beta cell differentiation and performing ChIP-seq on FEV+ endocrine progenitor stage cells identified transcriptional targets expected to mediate the transition from a pre-beta progenitor to a differentiated beta cell. A transcriptional map of FEV transcription factor activity enables modification of current in vitro beta cell differentiation protocols to one that promotes the expression of key FEV-regulated transcriptional circuits that promote beta cell differentiation from endocrine progenitors. Identification of transcriptional targets in hESC-derived FEV+ beta cells also illuminated the function of FEV in differentiated beta cells.
Suppressing the Formation of hESC-Derived Blocked Endocrine Progenitors
Tremendous effort has been devoted to determining the molecular cues that will make derivation of the beta lineage from hESCs more efficient. Disclosed herein is a hESC-derived FEV-expressing population that appeared to be mis-differentiated during in vitro beta cell differentiation. The top differentially-expressed gene of this blocked, FEV-expressing cell population was the transcription factor PHOX2A. Interestingly, PHOX2A was also previously reported to mark a non-endocrine population that emerged in in vitro beta cell differentiation but was not described as mis-directed in differentiation potential (Veres et al., 2019). PHOX2A is a pro-neural homeodomain transcription factor and a key regulator of neural progenitor differentiation into noradrenergic neurons of the central nervous system (CNS) and the peripheral nervous system (PNS) (Lo et al., 1998; Morin et al., 1997). Noradrenergic neurons are characterized by synthesis and storage of catecholamines, including norepinephrine, which serve as neurotransmitters (Hayashida and Eisenach, 2018). In this differentiation process, BMP2 and cyclic AMP (cAMP) signaling synergistically induce noradrenergic neuron differentiation through Phox2a transcription and Phox2a activation (Benjanirut et al., 2006; Chen et al., 2005; Paris et al., 2006). Given the expression of PHOX2A specifically in cells occupying the mis-differentiated trajectory in the pseudotemporal ordering analysis disclosed herein, it is expected that the PDX1+/NKX6.1+ pancreatic progenitors in the in vitro beta cell differentiation process have mis-differentiated towards this PHOX2A+ noradrenergic neural lineage.
Inhibition of BMP2 and cAMP signaling represents possible avenues through which in vitro beta cell differentiation can avoid entering this mis-directed differentiation path that resembled the noradrenergic neural lineage.
Reported Enterochromaffin Cells in In Vitro Beta Cell Differentiation
Currently, in vitro beta cell differentiation does not result in 100% purity of beta cells, and there are other cell types that arise during the directed differentiation of hESCs to the beta lineage. Recently, a population deemed enterochromaffin cells (ECs) has been described as arising in in vitro beta cell differentiation (Veres et al., 2019). ECs reside along the epithelial lining of the intestine and are the most abundant cell type among the enteroendocrine cells found in the intestine (Lund et al., 2018). The main functions of ECs are to regulate intestinal motility required for digestion and modulate the activity of the enteric nervous system through the production and secretion of the neurotransmitter serotonin. Although ECs make up less than 1% of the total intestinal epithelium, they produce more than 90% of the body's serotonin (Gershon, 2013; Mawe and Hoffman, 2013). Unlike neurons, ECs utilize tryptophan hydroxylase 1 (TPH1) and not TPH2 to synthesize serotonin, and instead of employing small neurosecretory vesicles, ECs store serotonin in large dense core vesicles (LDCVs) with the help of CHGA and CHGAB (Cote et al., 2003; Machado et al., 2010; Walther and Bader, 2003). Thus, ECs resemble lineages of both the nervous system and hormone-secreting pancreatic islets.
ECs are defined by the expression of markers that also are expressed by both serotonergic neurons and pancreatic endocrine cells. These markers include Fev, Lmx1a, Lmx1b, and Tph1 (Ding et al., 2003; Kiyasova and Gaspar, 2011; Liu et al., 2010; Maurer et al., 2004; Ohta et al., 2011; Wyler et al., 2016; Zhang et al., 2017). Proper differentiation of ECs in the intestine during development also requires transcription factors critical for pancreatic endocrine cell differentiation, including Ngn3 (Jenny et al., 2002; López-Díaz et al., 2007; Schonhoff et al., 2004), Nkx2.2 (Gross et al., 2016), Isl1 (Terry et al., 2014), NeuroD1 (Naya et al., 1997), Pax4 (Beucher et al., 2012a). Interestingly, Fev is also expressed by ECs but is not required for EC differentiation in mice (Wang et al., 2010b). Given the striking similarity in gene expression profiles of ECs and EC differentiation to those of pancreatic endocrine cells, it is not surprising to observe ECs generated in in vitro beta cell differentiation. The mis-differentiation of hESC-derived endocrine progenitors towards similar lineages, such as that of the EC, is not surprising, given that in vitro beta cell differentiation is not 100% efficient. It is likely that the ECs observed during in vitro beta cell differentiation represent another mis-differentiation process, similar to that observed with mis-differentiated PHOX2A+ cells. Given that these hESC-derived ECs also express FEV, these ECs could be mistaken for the FEV+ endocrine progenitors identified in human pancreatic development. However, this is not the case, given that a separate population of FEV+ hESC-derived endocrine progenitors is found that give rise to the beta lineage in the in vitro differentiation process disclosed herein. It is likely that these FEV+ ECs are derived from the same hESC-derived FEV+ endocrine progenitors that gave rise to beta cells.
All publications and patents mentioned in the application are herein incorporated by reference in their entireties or in relevant part, as would be apparent from context. Various modifications and variations of the disclosed subject matter will be apparent to those skilled in the art without departing from the scope and spirit of the disclosure. Although the disclosure has been described in connection with specific embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Various modifications of the described modes for making or using the disclosed subject matter that are obvious to those skilled in the relevant field(s) are intended to be within the scope of the following claims.
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This application claims the priority benefit under 35 U.S.C. § 119(e) of U.S. Provisional Patent Application No. 62/736,237, filed Sep. 25, 2018, the disclosure of which is incorporated herein by reference in its entirety.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US19/52958 | 9/25/2019 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 62736237 | Sep 2018 | US |
