The present disclosure relates to a virtual staining application used for biomarker discovery, tissue-based research studies, and diagnostic tests. More specifically, the present disclosure relates to a virtual staining methodology for the application of multiple immunohistochemical (“IHC”) staining techniques to the same tissue sample and same tissue location, independently, without requiring washing of the IHC stain or modification of the tissue. The virtual staining methodology described herein may also be combined with other existing virtual stains and conventional assay readouts to create new multiplexed readouts.
Immunohistochemical analyses are frequently used for evaluation and diagnosis of various diseases by clinicians and researchers across many fields of medicine and biology. Within these fields, there is a growing demand for use of biomarker information to evaluate in situ protein expression in tumor tissues. As discussed in Immunohistochemical Staining Characteristics of Nephrogenic Adenoma Using the PIN-4 Cocktail (p63, AMACR, and CK903) and GATA-3, McDaniel, A., et al., Am. J. Surg. Pathol. 2014 December; 38(12):1664-71, Immunohistochemical staining characteristics of nephrogenic adenoma using the PIN-4 cocktail (p63, AMACR, and CK903) and GATA-3 chromogenic IHC staining is the most widely used methodology in this field, but the methodology is technically limited due to the number of tissue samples required per panel of IHC stain. This limitation requires repeated sampling of scarce tissues, which is often lengthy, impossible due to patient risk of tissue excision, and may result in human error.
Furthermore, IHC staining techniques require physical staining of each individual tissue sample. These techniques use “heat deactivation” steps between the application of each stain or assay, whereby the high heat applied to the tissue completely denatures the previous antibody-enzyme complex rendering it inactive for the application of the next IHC stain or assay. The use of heat deactivation steps also degrades the morphology of the tissue making it unusable for additional IHC staining or assay readouts. IHC staining techniques and assay read outs are susceptible to human error, which may lead to previous antibody-enzyme complexes remaining active. Such situations can result in erroneous staining patterns, which can cause false positive or false negative results.
The risk of errors increases with the number of analytes tested in a single tissue sample. As discussed in “The use of immunohistochemistry for biomarker assessment—can it compete with other technologies” in Toxicology Pathology, despite being semi-automated, multiplexing staining methods still have many drawbacks. Multiplexing staining methods are complex, labor-intensive, and time-consuming. For example, the increased number of automated fluid dispensing steps in a multiplexed staining procedure may lead to non-staining events, where the reaction fails to occur due to a missed dispense of fluid. In this case, a false negative result may occur without detection by the reviewing pathologist. Furthermore, due to steric hindrance from the application of antibodies and dyes, including non-specific background staining, it is not possible to create a generalizable method (i.e. a method where the order of stains applied and matching of antibodies and dye colors is not interchangeable) for multiplex tissue staining. As discussed in “Multiplex Immunohistochemistry: The Importance of Staining Order When Producing a Validated Protocol” in Immunotherapy, markers such as CD3 and CD8, which are membrane stains with overlapping binding domains, interfere with the staining results of each individual marker. In this case, the study showed a 90% drop in the amount of CD8+ staining cells when combined in a multiplex stain with CD3 as compared to the individual stains. Furthermore, it was shown that a CD20 marker could not be combined in a multiplex panel since the clinical protocol for this marker does not require heat-induced epitope retrieval, while these steps are required as part of a conventional multiplexed assay.
Different approaches have been developed to address some of the limitations described above and improve overall workflow. Recently, computational staining techniques have been developed using deep learning approaches to virtually stain tissue samples. Virtually staining tissues enables researchers to use a virtual stain on a digital image of a tissue, thereby reducing the need for physical tissue samples and eliminating errors found in conventional staining methods and assay readouts. However, the current methods of virtual staining only allow for one virtual stain per digital tissue image, thereby limiting the types of analyses that can be run on the digital tissues.
In view of the foregoing, there is a need in the art for a multiplexing staining technique that reduces time, increases efficiency and accuracy, and allows for reuse of the biological tissue sample.
The objective of the disclosure is to provide a virtual staining technique that allows for virtual stain multiplexing using virtual stains as well as traditional stain and assay readouts. Virtual staining based on an embodiment of the disclosure allows for multiple stains and assays to be applied to the same tissue sample, independently and in combination, without the need for washing of the traditional stain or modification of the tissue. In this way, different stains and assays may be applied to the same location multiple times. The present disclosure takes advantage of the unique property of virtual staining that enables customization and combination of multiple histological staining techniques.
In one embodiment, the method of the present disclosure includes capturing an autofluorescence image of a physical tissue sample mounted on a slide. Multiple images of the physical tissue may be taken to obtain the most accurate representation of the tissue. Once the image of the slide is satisfactory, the digital image is forwarded to the standard image pre-processing pipeline to prepare for inference-based virtual staining. Using the autofluorescence images, one or more neural networks can be used to infer multiple virtual IHC stains for the issue section. Now that the digital image has been created and stored, the image may undergo a second round of pre-processing prior to undergoing multiplexing.
After completion of the second round of pre-processing, the digital image of the stained tissue undergoes multiplexing. Here, the counterstain and the antibody-associated stains of the image are converted to either pre-specified stain vectors or eigenvectors, calculated using the image. In an embodiment where more than one biomarker is used, the stain is separated into three or more images, depicting each virtual antibody-associated stain in a separate image. Each image containing the separated virtual antibody-associated stain is then recolored to ensure that the different biomarkers can be differentiated when recombined. The recolored virtual antibody-associated stains and the counterstain are then recombined into a single image, and converted from the optical density color space to the standard RGB color space, where all three colors can be visualized.
In another embodiment, the tissue can be stained with traditional IHC staining techniques to identify specific biomarkers within the tissue after the autofluorescence imaging of the label-free tissue, if desired. Once the tissue is stained with a traditional antibody-associated stain or assay techniques, including but not limited to, 3,3′-diaminobenzidine (“DAB”) staining, mass spectroscopy, sequencing, and In Situ Hybridization (“ISH”). The IHC staining technique produces a stained tissue sample, which is then transferred onto a microscope slide having a barcode label, and covered with a coverslip for imaging. The slide undergoes brightfield or fluorescence whole slide imaging to obtain a digital image of the slide. The image undergoes quality control (“QC”) to identify images of tissue that would be unsuitable for processing. For example, instances including, but not limited to, where the image is evaluated for out-of-focus areas, missing tissue, cell count, necrosis, and other like features. Once the digital image of the slide is satisfactory, the digital image is forwarded to the same image pre-processing pipeline as the virtually stained tissues, and the components of the stain are computationally separated. These separated stains can then be merged with the virtual stain in the same manner described above.
The visualization of the staining combination can be customized according to the needs of any specific user. The colors used for each of the combined stains can be individually changed, and the intensity and opacity of each stain can be customized. This process may be performed in real time using a standard computer, allowing the user to immediately see the effect of the color changes on the tissue.
In another embodiment, an alternate process can be applied which does not require any deconvolution of existing stains during inference. Instead, the virtual staining networks can be trained to solely generate individual stains (i.e. only a counterstain, or only the antibody-associated stain) instead of generating a traditional mixed stain. These individual stains can then be combined based on the user's preferences. In a further embodiment, the virtual stains can also be generated using other modalities, such as brightfield images of stained tissue and used as the input of to the neural network to infer the images of the virtual IHCs. Additional imaging modalities that could be used to generate the virtual stains include, but are not limited to, brightfield, darkfield, fluorescence lifetime, Raman, hyperspectral, and harmonic generation microscopy along with phase imaging techniques and others used to image both labelled and label-free tissue.
In another embodiment an alternate process can be used as a framework that directly utilizes the virtual stains as an accurate means of providing annotations as input for a machine learning project. The quality of the annotations directly relates to the trained model prediction accuracy. Highly accurate virtual staining can delineate between subpopulations of cells, cells' state and signaling. Information from other channels, such as additional stains, sequencing or proteomic data may be used to provide accurate and rich annotations. This alternative workflow provides an advantage over the current tedious and expensive annotation process.
An inherent benefit of multiplexing with virtual stains from the same tissue section is the direct readiness for analytics. With conventional approaches to multiplexing, individual biomarker channels must be unmixed, have cell segmentation applied, and then determine presence or absence of biomarkers on specific cells. This computation is inefficient and may result in false positive or negative cells. In the virtual multiplex approach, each biomarker stain is individually rendered, ready for calling biomarker status without unmixing. In addition, there are no physical, chemical, or biological limitations to the number of virtual stains and assays that may be rendered within the same cell or cellular compartment, unlike conventional multiplex immunohistochemistry (“mIHC”) where steric hindrance and other effects limit the ability to apply multiple IHC stains.
The staining combinations produced by the virtual stain multiplexing in accordance with the present disclosure allow for novel workflows and numerous combined readouts. The expected benefits of the virtual staining methodology include advanced insights into cellular structure, protein targeting, diagnostic testing, and disease.
Furthermore, this disclosure enables stain customization according to user needs, including the specific configurations and intensities of multiple combined stains. Images of conventional IHC stains and virtual IHC stains can be separated into their individual constituents, and then merged in any possible combination. In so doing, the disclosure utilizes patterns from endogenous signals to digitally generate the tissue staining patterns arising from assays developed with an antibody, but do not require the use of the antibody or test kit in the deployed product.
It should be appreciated that the subject technology can be implemented and utilized in numerous ways, including, without limitation, as a process, an apparatus, a system, a device, a method for applications now known and later developed such as a computer readable medium and a hardware device specifically designed to accomplish the features and functions of the subject technology. These and other unique features of the system disclosed herein will become more readily apparent from the following description and the accompanying drawings.
The accompanying drawings, referred to herein and constituting a part hereof, illustrate preferred embodiments of the disclosure and, together with the description, serve to explain the principles of the disclosure.
The advantages, and other features of the method disclosed herein, will become more readily apparent to those having ordinary skill in the art from the following detailed description of certain preferred embodiments taken in conjunction with the drawings, which set forth representative embodiments of the present disclosure and wherein like reference numerals identify similar structural elements. It is understood that references to the figures such as up, down, upward, downward, left, and right are with respect to the figures and not meant in a limiting sense.
For each biomarker of interest in the virtual multiplex panel, a network is designed and trained using single biomarker stains, and includes a nuclear counterstain. The network of inference-based virtual stains then produces virtually stained images with each biomarker of interest along with a nuclear counterstain. As seen in
In a second embodiment 200, illustrated in
In this embodiment, the network of inference-based virtual staining produces two output image files, one of the pure biomarker stain and a second of a corresponding nuclear counterstain. In this way, the network of inference-based virtual stains unmixes the images of biomarker stain from the nuclear counterstain.
In the embodiment of
In a fourth embodiment 400 of the disclosure, illustrated in
As illustrated in
In a fifth embodiment, illustrated in
In another embodiment, illustrated in
An inherent benefit of multiplexing with virtual stains from the same tissue section is the direct readiness for analytics. With conventional approaches to multiplexing, individual biomarker channels must be unmixed, have cell segmentation applied, and then determine presence or absence of biomarkers on specific cells. The conventional computation is inefficient and may result in false positive or negative cells called for their biomarker status. In the virtual multiplex approach, each biomarker stain is individually rendered, ready for calling biomarker status without unmixing.
At the same time, the staining protocol database 1108 releases a panel of virtual stains, which comprise the virtual multiplexed staining result. This input is combined with pre-processing of the autofluorescence images done in step 1117, and at step 1118 where inference-based multiplex virtual staining is performed. Example methods of step 1118 are disclosed in
In parallel with step 1212, the output of step 1205 is a stack of unmixed virtual and convention biomarker stains seen in step 1213, which may be combined as a stack of images, including a single nuclear counterstain image in step 1214. This fully unmixed set of conventional and virtual biomarker stains, and a counterstain is suitable for further image analysis using conventional methods or to linearly remix for visualization of the combined staining patterns.
The mass storage 1408 may include one or more magnetic disk, optical disk drives, and/or solid state memories, for storing data and instructions for use by the CPU 1402. At least one component of the mass storage system 1408, preferably in the form of a non-volatile disk drive, solid state, or tape drive, stores the database used for processing data and controlling functions of the neural network for inference-based virtual staining. The mass storage system 1408 may also include one or more drives for various portable media, such as a floppy disk, flash drive, a compact disc read only memory (CD-ROM, DVD, CD-RW, and variants), memory stick, or an integrated circuit non-volatile memory adapter (i.e. PC-MCIA adapter) to input and output data and code to and from the computer system 200.
The computer system 1400 may also include one or more input/output interfaces for communications, shown by way of example, as interface 1410 and/or a transceiver for data communications via the network 1412. The data interface 1410 may be a modem, an Ethernet card, or any other suitable data communications device. To provide the functions of a processor running the neural network for inference-based virtual staining, the data interface 1410 may provide a relatively high-speed link to a network 1412, such as an intranet, internet, or the Internet, either directly or through another external interface. The communication link to the network 1412 may be, for example, optical, wired, or wireless (e.g., via satellite or cellular network). The computer system 1400 may also connect via the data interface 1410 and network 1412 to at least one other computer system to perform remote or distributed multi-sensor processing related to, for example, a common operational picture (COP). Alternatively, the computer system 1400 may include a mainframe or other type of host computer system capable of Web-based communications via the network 1412. The computer system 1400 may include software for operating a network application such as a web server and/or web client.
The computer system 1400 may also include suitable input/output ports, that may interface with a portable data storage device, or use the interconnect bus 1406 for interconnection with a local display 1416 and keyboard 1414 or the like serving as a local user interface for programming and/or data retrieval purposes. The display 1416 may include a touch screen capability to enable users to interface with the system 1400 by touching portions of the surface of the display 1416. Server operations personnel may interact with the system 1400 for controlling and/or programming the system from remote terminal devices via the network 1412.
The computer system 1400 may run a variety of application programs and store associated data in a database of mass storage system 1408. One or more such applications may include a neural network for inference-based virtual staining such as described with respect to
The components contained in the computer system 1400 may enable the computer system to be used as a server, workstation, personal computer, network terminal, mobile computing device, mobile telephone, System on a Chip (SoC), and the like. The system 1400 may include software and/or hardware that implements a web server application. The web server application may include software such as HTML, XML, WML, SGML, PHP (Hypertext Preprocessor), CGI, and like languages.
The foregoing features of the disclosure may be realized as a software component operating in the system 1400 where the system 1400 includes Unix workstation, a Windows workstation, a LINUX workstation, or other type of workstation. Other operation systems may be employed such as, without limitation, Windows, MAC OS, and LINUX. In some aspects, the software can optionally be implemented as a C language computer program, or a computer program written in any high level language including, without limitation, Javascript, Java, CSS, Python, Keras, TensorFlow, PHP, Ruby, C++, C, Shell, C#, Objective-C, Go, R, TeX, VimL, Perl, Scala, CoffeeScript, Emacs Lisp, Swift, Fortran, or Visual BASIC. Certain script-based programs may be employed such as XML, WML, PHP, and so on. The system 200 may use a digital signal processor (DSP).
As stated previously, the mass storage 1408 may include a database. The database may be any suitable database system, including the commercially available Microsoft Access database, and can be a local or distributed database system. A database system may implement Sybase and/or a SQL Server. The database may be supported by any suitable persistent data memory, such as a hard disk drive, RAID system, tape drive system, floppy diskette, or any other suitable system. The system 1400 may include a database that is integrated with the neural network for inference-based virtual staining, however, it will be understood that, in other implementations, the database and mass storage 1408 can be an external element.
In certain implementations, the system 1400 may include an Internet browser program and/or be configured operate as a web server. In some configurations, the client and/or web server may be configured to recognize and interpret various network protocols that may be used by a client or server program. Commonly used protocols include Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), Telnet, and Secure Sockets Layer (SSL), and Transport Layer Security (TLS), for example. However, new protocols and revisions of existing protocols may be frequently introduced. Thus, in order to support a new or revised protocol, a new revision of the server and/or client application may be continuously developed and released.
In one implementation, the neural network includes a network-based, e.g., Internet-based, application that may be configured and run on the system 1400 and/or any combination of the other components of the neural network for inference-based virtual staining. The computer system 1400 may include a web server running a Web 2.0 application or the like. Web applications running on the neural network may use server-side dynamic content generation mechanisms such, without limitation, Java servlets, CGI, PHP, or ASP. In certain implementations, mashed content may be generated by a web browser running, for example, client-side scripting including, without limitation, JavaScript and/or applets on a wireless device.
In certain implementations, the neural network for inference-based virtual staining or computer system 1400 may include applications that employ asynchronous JavaScript+XML (Ajax) and like technologies that use asynchronous loading and content presentation techniques. These techniques may include, without limitation, XHTML and CSS for style presentation, document object model (DOM) API exposed by a web browser, asynchronous data exchange of XML data, and web browser side scripting, e.g., JavaScript. Certain web-based applications and services may utilize web protocols including, without limitation, the services-orientated access protocol (SOAP) and representational state transfer (REST). REST may utilize HTTP with XML.
The neural network for inference-based virtual staining, computer system 1400, or another component of neural network may also provide enhanced security and data encryption. Enhanced security may include access control, biometric authentication, cryptographic authentication, message integrity checking, encryption, digital rights management services, and/or other like security services. The security may include protocols such as IPSEC and IKE. The encryption may include, without limitation, DES, 3DES, AES, RSA, ECC, and any like public key or private key based schemes.
It will be appreciated by those of ordinary skill in the pertinent art that the functions of several elements may, in alternative embodiments, be carried out by fewer elements, or a single element. Similarly, in some embodiments, any functional element may perform fewer, or different, operations than those described with respect to the illustrated embodiment. Also, functional elements shown as distinct for purposes of illustration may be incorporated within other functional elements in a particular implementation (e.g., modules, databases, interfaces, computers, servers and the like may perform any combination of functional elements).
While the subject technology has been described with respect to preferred embodiments, those skilled in the art will readily appreciate that various changes and/or modifications can be made to the subject technology without departing from the spirit or scope of the subject disclosure. The appended claims are exemplary and may be combined and arranged in any manner including with multiple dependencies and the like.
This application claims the benefit of U.S. Provisional Application No. 63/349,383 filed Jun. 6, 2022 entitled VIRTUAL STAIN MULTIPLEXING USING LINEAR COMPUTATIONAL TRANSFORMS, and claims the benefit of U.S. Provisional Application No. 63/419,871 filed Oct. 27, 2022 entitled METHOD OF GENERATING INFERENCE-BASED VIRTUALLY STAINED IMAGE ANNOTATIONS, the contents of which are incorporated herein by reference in their entirety
Number | Date | Country | |
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63419871 | Oct 2022 | US | |
63349383 | Jun 2022 | US |