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Johann DJ, Shin IJ, Roberge A, Laun S, Peterson EA, Liu M, Steliga MA, Muesse J, Emmert-Buck MR, Tangrea MA. Effect of Antigen Retrieval on Genomic DNA From Immunodissected Samples. J Histochem Cytochem 2022; 70:643-658. [PMID: 36129255 PMCID: PMC9527476 DOI: 10.1369/00221554221124163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 08/12/2022] [Indexed: 11/22/2022] Open
Abstract
Immunohistochemical (IHC) staining is an established technique for visualizing proteins in tissue sections for research studies and clinical applications. IHC is increasingly used as a targeting strategy for procurement of labeled cells via tissue microdissection, including immunodissection, computer-aided laser dissection (CALD), expression microdissection (xMD), and other techniques. The initial antigen retrieval (AR) process increases epitope availability and improves staining characteristics; however, the procedure can damage DNA. To better understand the effects of AR on DNA quality and quantity in immunodissected samples, both clinical specimens (KRAS gene mutation positive cases) and model system samples (lung cancer patient-derived xenograft tissue) were subjected to commonly employed AR methods (heat induced epitope retrieval [HIER], protease digestion) and the effects on DNA were assessed by Qubit, fragment analysis, quantitative PCR, digital droplet PCR (ddPCR), library preparation, and targeted sequencing. The data showed that HIER resulted in optimal IHC staining characteristics, but induced significant damage to DNA, producing extensive fragmentation and decreased overall yields. However, neither of the AR methods combined with IHC prevented ddPCR amplification of small amplicons and gene mutations were successfully identified from immunodissected clinical samples. The results indicate for the first time that DNA recovered from immunostained slides after standard AR and IHC processing can be successfully employed for genomic mutation analysis via ddPCR and next-generation sequencing (NGS) short-read methods.
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Affiliation(s)
- Donald J. Johann
- Winthrop P. Rockefeller Cancer Institute,
University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Ik Jae Shin
- Winthrop P. Rockefeller Cancer Institute,
University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | | | - Sarah Laun
- Avoneaux Medical Institute, Baltimore,
Maryland
- Alvin & Lois Lapidus Cancer Institute,
Sinai Hospital of Baltimore, LifeBridge Health, Baltimore, Maryland
| | - Erich A. Peterson
- Winthrop P. Rockefeller Cancer Institute,
University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Meei Liu
- Winthrop P. Rockefeller Cancer Institute,
University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Matthew A. Steliga
- Winthrop P. Rockefeller Cancer Institute,
University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Jason Muesse
- Winthrop P. Rockefeller Cancer Institute,
University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | | | - Michael A. Tangrea
- Alvin & Lois Lapidus Cancer Institute,
Sinai Hospital of Baltimore, LifeBridge Health, Baltimore, Maryland
- Biology Department, Loyola University
Maryland, Baltimore, Maryland
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Liotta LA, Pappalardo PA, Carpino A, Haymond A, Howard M, Espina V, Wulfkuhle J, Petricoin E. Laser Capture Proteomics: spatial tissue molecular profiling from the bench to personalized medicine. Expert Rev Proteomics 2021; 18:845-861. [PMID: 34607525 PMCID: PMC10720974 DOI: 10.1080/14789450.2021.1984886] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 09/21/2021] [Indexed: 10/20/2022]
Abstract
INTRODUCTION Laser Capture Microdissection (LCM) uses a laser to isolate, or capture, specific cells of interest in a complex heterogeneous tissue section, under direct microscopic visualization. Recently, there has been a surge of publications using LCM for tissue spatial molecular profiling relevant to a wide range of research topics. AREAS COVERED We summarize the many advances in tissue Laser Capture Proteomics (LCP) using mass spectrometry for discovery, and protein arrays for signal pathway network mapping. This review emphasizes: a) transition of LCM phosphoproteomics from the lab to the clinic for individualized cancer therapy, and b) the emerging frontier of LCM single cell molecular analysis combining proteomics with genomic, and transcriptomic analysis. The search strategy was based on the combination of MeSH terms with expert refinement. EXPERT OPINION LCM is complemented by a rich set of instruments, methodology protocols, and analytical A.I. (artificial intelligence) software for basic and translational research. Resolution is advancing to the tissue single cell level. A vision for the future evolution of LCM is presented. Emerging LCM technology is combining digital and AI guided remote imaging with automation, and telepathology, to a achieve multi-omic profiling that was not previously possible.
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Affiliation(s)
- Lance A. Liotta
- Center For Applied Proteomics and Molecular Medicine (CAPMM) School of Systems Biology, College of Sciences, George Mason University, Manassas, VA 20110, USA
| | - Philip A. Pappalardo
- Center For Applied Proteomics and Molecular Medicine (CAPMM) School of Systems Biology, College of Sciences, George Mason University, Manassas, VA 20110, USA
| | - Alan Carpino
- Fluidigm Corporation, South San Francisco, CA, USA
| | - Amanda Haymond
- Center For Applied Proteomics and Molecular Medicine (CAPMM) School of Systems Biology, College of Sciences, George Mason University, Manassas, VA 20110, USA
| | - Marissa Howard
- Center For Applied Proteomics and Molecular Medicine (CAPMM) School of Systems Biology, College of Sciences, George Mason University, Manassas, VA 20110, USA
| | - Virginia Espina
- Center For Applied Proteomics and Molecular Medicine (CAPMM) School of Systems Biology, College of Sciences, George Mason University, Manassas, VA 20110, USA
| | - Julie Wulfkuhle
- Center For Applied Proteomics and Molecular Medicine (CAPMM) School of Systems Biology, College of Sciences, George Mason University, Manassas, VA 20110, USA
| | - Emanuel Petricoin
- Center For Applied Proteomics and Molecular Medicine (CAPMM) School of Systems Biology, College of Sciences, George Mason University, Manassas, VA 20110, USA
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Hipp JD, Johann DJ, Chen Y, Madabhushi A, Monaco J, Cheng J, Rodriguez-Canales J, Stumpe MC, Riedlinger G, Rosenberg AZ, Hanson JC, Kunju LP, Emmert-Buck MR, Balis UJ, Tangrea MA. Computer-Aided Laser Dissection: A Microdissection Workflow Leveraging Image Analysis Tools. J Pathol Inform 2018; 9:45. [PMID: 30622835 PMCID: PMC6298131 DOI: 10.4103/jpi.jpi_60_18] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 10/16/2018] [Indexed: 01/05/2023] Open
Abstract
Introduction The development and application of new molecular diagnostic assays based on next-generation sequencing and proteomics require improved methodologies for procurement of target cells from histological sections. Laser microdissection can successfully isolate distinct cells from tissue specimens based on visual selection for many research and clinical applications. However, this can be a daunting task when a large number of cells are required for molecular analysis or when a sizeable number of specimens need to be evaluated. Materials and Methods To improve the efficiency of the cellular identification process, we describe a microdissection workflow that leverages recently developed and open source image analysis algorithms referred to as computer-aided laser dissection (CALD). CALD permits a computer algorithm to identify the cells of interest and drive the dissection process. Results We describe several "use cases" that demonstrate the integration of image analytic tools probabilistic pairwise Markov model, ImageJ, spatially invariant vector quantization (SIVQ), and eSeg onto the ThermoFisher Scientific ArcturusXT and Leica LMD7000 microdissection platforms. Conclusions The CALD methodology demonstrates the integration of image analysis tools with the microdissection workflow and shows the potential impact to clinical and life science applications.
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Affiliation(s)
- Jason D Hipp
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, USA.,Google Inc., Mountain View, CA, USA
| | - Donald J Johann
- Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Yun Chen
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, USA.,Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | | | - Jerome Cheng
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Jaime Rodriguez-Canales
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, USA.,Medimmune, LLC, Gaithersburg, MD, USA
| | | | - Greg Riedlinger
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, USA.,Division of Translational Pathology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Avi Z Rosenberg
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, USA.,Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Jeffrey C Hanson
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, USA
| | - Lakshmi P Kunju
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Michael R Emmert-Buck
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, USA.,Avoneaux Medical Institute, LLC, Baltimore, MD, USA
| | - Ulysses J Balis
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Michael A Tangrea
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, USA.,Alvin and Lois Lapidus Cancer Institute, Sinai Hospital of Baltimore, LifeBridge Health, Baltimore, MD, USA
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5
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Frost AR, Eltoum I, Siegal GP, Emmert‐Buck MR, Tangrea MA. Laser Microdissection. ACTA ACUST UNITED AC 2015; 112:25A.1.1-25A.1.30. [DOI: 10.1002/0471142727.mb25a01s112] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Andra R. Frost
- Department of Pathology, University of Alabama at Birmingham Birmingham Alabama
| | - Isam‐Eldin Eltoum
- Department of Pathology, University of Alabama at Birmingham Birmingham Alabama
| | - Gene P. Siegal
- Department of Pathology, University of Alabama at Birmingham Birmingham Alabama
| | | | - Michael A. Tangrea
- Alvin & Lois Lapidus Cancer Institute, Sinai Hospital Baltimore Maryland
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6
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Oh EY, Christensen SM, Ghanta S, Jeong JC, Bucur O, Glass B, Montaser-Kouhsari L, Knoblauch NW, Bertos N, Saleh SM, Haibe-Kains B, Park M, Beck AH. Extensive rewiring of epithelial-stromal co-expression networks in breast cancer. Genome Biol 2015; 16:128. [PMID: 26087699 PMCID: PMC4471934 DOI: 10.1186/s13059-015-0675-4] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2015] [Accepted: 05/13/2015] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Epithelial-stromal crosstalk plays a critical role in invasive breast cancer pathogenesis; however, little is known on a systems level about how epithelial-stromal interactions evolve during carcinogenesis. RESULTS We develop a framework for building genome-wide epithelial-stromal co-expression networks composed of pairwise co-expression relationships between mRNA levels of genes expressed in the epithelium and stroma across a population of patients. We apply this method to laser capture micro-dissection expression profiling datasets in the setting of breast carcinogenesis. Our analysis shows that epithelial-stromal co-expression networks undergo extensive rewiring during carcinogenesis, with the emergence of distinct network hubs in normal breast, and estrogen receptor-positive and estrogen receptor-negative invasive breast cancer, and the emergence of distinct patterns of functional network enrichment. In contrast to normal breast, the strongest epithelial-stromal co-expression relationships in invasive breast cancer mostly represent self-loops, in which the same gene is co-expressed in epithelial and stromal regions. We validate this observation using an independent laser capture micro-dissection dataset and confirm that self-loop interactions are significantly increased in cancer by performing computational image analysis of epithelial and stromal protein expression using images from the Human Protein Atlas. CONCLUSIONS Epithelial-stromal co-expression network analysis represents a new approach for systems-level analyses of spatially localized transcriptomic data. The analysis provides new biological insights into the rewiring of epithelial-stromal co-expression networks and the emergence of epithelial-stromal co-expression self-loops in breast cancer. The approach may facilitate the development of new diagnostics and therapeutics targeting epithelial-stromal interactions in cancer.
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Affiliation(s)
- Eun-Yeong Oh
- Cancer Research Institute, Beth Israel Deaconess Cancer Center, Boston, MA, 02215, USA. .,Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA. .,Harvard Medical School, Boston, MA, 02215, USA.
| | - Stephen M Christensen
- Cancer Research Institute, Beth Israel Deaconess Cancer Center, Boston, MA, 02215, USA. .,Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA. .,Harvard Medical School, Boston, MA, 02215, USA.
| | - Sindhu Ghanta
- Cancer Research Institute, Beth Israel Deaconess Cancer Center, Boston, MA, 02215, USA. .,Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA. .,Harvard Medical School, Boston, MA, 02215, USA.
| | - Jong Cheol Jeong
- Cancer Research Institute, Beth Israel Deaconess Cancer Center, Boston, MA, 02215, USA. .,Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA. .,Harvard Medical School, Boston, MA, 02215, USA.
| | - Octavian Bucur
- Cancer Research Institute, Beth Israel Deaconess Cancer Center, Boston, MA, 02215, USA. .,Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA. .,Harvard Medical School, Boston, MA, 02215, USA.
| | - Benjamin Glass
- Cancer Research Institute, Beth Israel Deaconess Cancer Center, Boston, MA, 02215, USA. .,Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA. .,Harvard Medical School, Boston, MA, 02215, USA.
| | - Laleh Montaser-Kouhsari
- Cancer Research Institute, Beth Israel Deaconess Cancer Center, Boston, MA, 02215, USA. .,Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA. .,Harvard Medical School, Boston, MA, 02215, USA.
| | - Nicholas W Knoblauch
- Cancer Research Institute, Beth Israel Deaconess Cancer Center, Boston, MA, 02215, USA. .,Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA. .,Harvard Medical School, Boston, MA, 02215, USA.
| | - Nicholas Bertos
- Goodman Cancer Research Centre, McGill University, Montreal, QC, Canada.
| | - Sadiq Mi Saleh
- Goodman Cancer Research Centre, McGill University, Montreal, QC, Canada.
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, M5G 1L7, Canada. .,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, M5G 1L7, Canada.
| | - Morag Park
- Goodman Cancer Research Centre, McGill University, Montreal, QC, Canada.
| | - Andrew H Beck
- Cancer Research Institute, Beth Israel Deaconess Cancer Center, Boston, MA, 02215, USA. .,Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA. .,Harvard Medical School, Boston, MA, 02215, USA.
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Emerson T, Kirby M, Bethel K, Kolatkar A, Luttgen M, O'Hara S, Newton P, Kuhn P. Fourier-ring descriptor to characterize rare circulating cells from images generated using immunofluorescence microscopy. Comput Med Imaging Graph 2014; 40:70-87. [PMID: 25456146 DOI: 10.1016/j.compmedimag.2014.10.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2014] [Revised: 05/22/2014] [Accepted: 10/06/2014] [Indexed: 12/26/2022]
Abstract
We address the problem of subclassification of rare circulating cells using data driven feature selection from images of candidate circulating tumor cells from patients diagnosed with breast, prostate, or lung cancer. We determine a set of low level features which can differentiate among candidate cell types. We have implemented an image representation based on concentric Fourier rings (FRDs) which allow us to exploit size variations and morphological differences among cells while being rotationally invariant. We discuss potential clinical use in the context of treatment monitoring for cancer patients with metastatic disease.
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Affiliation(s)
- Tegan Emerson
- Department of Mathematics, Colorado State University, 841 Oval Drive, Fort Collins, CO 80523, United States.
| | - Michael Kirby
- Department of Mathematics, Colorado State University, 841 Oval Drive, Fort Collins, CO 80523, United States.
| | - Kelly Bethel
- The Scripps Clinic, Department of Pathology, 10666 N Torrey Pines Road, La Jolla, CA 92037, United States.
| | - Anand Kolatkar
- The Scripps Research Institute, The Kuhn Lab, 10550 N Torrey Pines Road, La Jolla, CA 92037, United States.
| | - Madelyn Luttgen
- The Scripps Research Institute, The Kuhn Lab, 10550 N Torrey Pines Road, La Jolla, CA 92037, United States.
| | - Stephen O'Hara
- DigitalGlobe, Image Mining Group, 1601 Dry Creek Drive, Longmont, CO 80503, United States.
| | - Paul Newton
- Department of Aerospace and Mechanical Engineering, University of Southern California Viterbi School of Engineering, Los Angeles, CA 90089, United States.
| | - Peter Kuhn
- The Kuhn Lab, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, United States.
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8
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Hipp JD, Cheng J, Hanson JC, Rosenberg AZ, Emmert-Buck MR, Tangrea MA, Balis UJ. SIVQ-LCM protocol for the ArcturusXT instrument. J Vis Exp 2014. [PMID: 25078867 DOI: 10.3791/51662] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
SIVQ-LCM is a new methodology that automates and streamlines the more traditional, user-dependent laser dissection process. It aims to create an advanced, rapidly customizable laser dissection platform technology. In this report, we describe the integration of the image analysis software Spatially Invariant Vector Quantization (SIVQ) onto the ArcturusXT instrument. The ArcturusXT system contains both an infrared (IR) and ultraviolet (UV) laser, allowing for specific cell or large area dissections. The principal goal is to improve the speed, accuracy, and reproducibility of the laser dissection to increase sample throughput. This novel approach facilitates microdissection of both animal and human tissues in research and clinical workflows.
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Affiliation(s)
- Jason D Hipp
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health
| | | | - Jeffrey C Hanson
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health
| | - Avi Z Rosenberg
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health
| | | | - Michael A Tangrea
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health;
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9
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Rodriguez-Canales J, Hanson JC, Hipp JD, Balis UJ, Tangrea MA, Emmert-Buck MR, Bova GS. Optimal molecular profiling of tissue and tissue components: defining the best processing and microdissection methods for biomedical applications. Methods Mol Biol 2013; 980:61-120. [PMID: 23359150 DOI: 10.1007/978-1-62703-287-2_5] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Isolation of well-preserved pure cell populations is a prerequisite for sound studies of the molecular basis of any tissue-based biological phenomenon. This updated chapter reviews current methods for obtaining anatomically specific signals from molecules isolated from tissues, a basic requirement for productive linking of phenotype and genotype. The quality of samples isolated from tissue and used for molecular analysis is often glossed over or omitted from publications, making interpretation and replication of data difficult or impossible. Fortunately, recently developed techniques allow life scientists to better document and control the quality of samples used for a given assay, creating a foundation for improvement in this area. Tissue processing for molecular studies usually involves some or all of the following steps: tissue collection, gross dissection/identification, fixation, processing/embedding, storage/archiving, sectioning, staining, microdissection/annotation, and pure analyte labeling/identification and quantification. We provide a detailed comparison of some current tissue microdissection technologies and provide detailed example protocols for tissue component handling upstream and downstream from microdissection. We also discuss some of the physical and chemical issues related to optimal tissue processing and include methods specific to cytology specimens. We encourage each laboratory to use these as a starting point for optimization of their overall process of moving from collected tissue to high-quality, appropriately anatomically tagged scientific results. Improvement in this area will significantly increase life science quality and productivity. The chapter is divided into introduction, materials, protocols, and notes subheadings. Because many protocols are covered in each of these sections, information relating to a single protocol is not contiguous. To get the greatest benefit from this chapter, readers are advised to read through the entire chapter first, identify protocols appropriate to their laboratory for each step in their workflow, and then reread entries in each section pertaining to each of these single protocols.
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Affiliation(s)
- Jaime Rodriguez-Canales
- Laser Capture Microdissection (LCM) Core, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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Roy Chowdhuri S, Hanson J, Cheng J, Rodriguez-Canales J, Fetsch P, Balis U, Filie AC, Giaccone G, Emmert-Buck MR, Hipp JD. Semiautomated laser capture microdissection of lung adenocarcinoma cytology samples. Acta Cytol 2012. [PMID: 23207440 DOI: 10.1159/000342984] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
OBJECTIVE In the past decade molecular diagnostics has changed the clinical management of lung adenocarcinoma patients. Molecular diagnostics, however, is largely dependent on the quantity and quality of the tumor DNA that is retrieved from the tissue or cytology samples. Frequently, patients are diagnosed on cytology specimens where the tumor cells are scattered within the cell block, making selecting for tumor enrichment difficult. In the past we have used laser capture microdissection (LCM) to select for pure populations of tumor cells to increase the sensitivity of molecular assays. This study explores several methods for semiautomated computer-guided LCM. STUDY DESIGN Hematoxylin and eosin- or TTF-1-immunostained slides from a pleural effusion cell block with metastatic lung adenocarcinoma were used for LCM with either AutoScan or a recently described pattern-matching algorithm, spatially invariant vector quantization (SIVQ), to define morphologic predicates (vectors) to select cells of interest. RESULTS We retrieved pure populations of tumor cells using both algorithm-guided LCM approaches with slight variations in cellular retrievals. Both methods were semiautomated, requiring minimum technical supervision. CONCLUSION In this study we demonstrate the first semiautomated, computer-guided LCM of a cytology specimen using SIVQ and AutoScan, a first step towards the long-term goal of integrating LCM into the clinical cytology-molecular workflow.
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Hipp JA, Hipp JD, Lim M, Sharma G, Smith LB, Hewitt SM, Balis UGJ. Image microarrays derived from tissue microarrays (IMA-TMA): New resource for computer-aided diagnostic algorithm development. J Pathol Inform 2012; 3:24. [PMID: 22934237 DOI: 10.4103/2153-3539.98168] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2012] [Accepted: 05/01/2012] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND Conventional tissue microarrays (TMAs) consist of cores of tissue inserted into a recipient paraffin block such that a tissue section on a single glass slide can contain numerous patient samples in a spatially structured pattern. Scanning TMAs into digital slides for subsequent analysis by computer-aided diagnostic (CAD) algorithms all offers the possibility of evaluating candidate algorithms against a near-complete repertoire of variable disease morphologies. This parallel interrogation approach simplifies the evaluation, validation, and comparison of such candidate algorithms. A recently developed digital tool, digital core (dCORE), and image microarray maker (iMAM) enables the capture of uniformly sized and resolution-matched images, with these representing key morphologic features and fields of view, aggregated into a single monolithic digital image file in an array format, which we define as an image microarray (IMA). We further define the TMA-IMA construct as IMA-based images derived from whole slide images of TMAs themselves. METHODS Here we describe the first combined use of the previously described dCORE and iMAM tools, toward the goal of generating a higher-order image construct, with multiple TMA cores from multiple distinct conventional TMAs assembled as a single digital image montage. This image construct served as the basis of the carrying out of a massively parallel image analysis exercise, based on the use of the previously described spatially invariant vector quantization (SIVQ) algorithm. RESULTS Multicase, multifield TMA-IMAs of follicular lymphoma and follicular hyperplasia were separately rendered, using the aforementioned tools. Each of these two IMAs contained a distinct spectrum of morphologic heterogeneity with respect to both tingible body macrophage (TBM) appearance and apoptotic body morphology. SIVQ-based pattern matching, with ring vectors selected to screen for either tingible body macrophages or apoptotic bodies, was subsequently carried out on the differing TMA-IMAs, with attainment of excellent discriminant classification between the two diagnostic classes. CONCLUSION The TMA-IMA construct enables and accelerates high-throughput multicase, multifield based image feature discovery and classification, thus simplifying the development, validation, and comparison of CAD algorithms in settings where the heterogeneity of diagnostic feature morphologic is a significant factor.
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Affiliation(s)
- Jennifer A Hipp
- Department of Pathology, University of Michigan, M4233A Medical Science I, 1301 Catherine Ann Arbor, Michigan 48109-0602
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12
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Sempere LF. Integrating contextual miRNA and protein signatures for diagnostic and treatment decisions in cancer. Expert Rev Mol Diagn 2012; 11:813-27. [PMID: 22022944 DOI: 10.1586/erm.11.69] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The promise of personalized medicine is highly dependent on the identification of biomarkers that inform diagnostic decisions and treatment options, as well as on the accurate, rapid and cost-effective detection and interpretation of these biomarkers. miRNAs, which are short noncoding regulatory RNAs, are rapidly emerging as a novel class of biomarkers with a unique set of biological and chemical properties that makes them very appealing candidates for theranostic applications in cancer. Since the utility of some protein-encoding gene biomarkers is already exploited in routine clinical practice, it will be important to identify areas in which miRNAs provide complementary or superior information to these existing (and other translational) biomarkers to enhance the diagnostic, prognostic and predictive power of molecular characterization of tumors. In this article, the challenges and opportunities for integration of miRNA-based assays in the clinical toolkit to improve care and management of patients afflicted with solid tumors will be discussed.
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Affiliation(s)
- Lorenzo F Sempere
- Department of Medicine, Rubin 763 HB7936, Norris Cotton Cancer Center, 1 Medical Center Drive, Lebanon, NH 03756-1000, USA.
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13
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Cooper LAD, Carter AB, Farris AB, Wang F, Kong J, Gutman DA, Widener P, Pan TC, Cholleti SR, Sharma A, Kurc TM, Brat DJ, Saltz JH. Digital Pathology: Data-Intensive Frontier in Medical Imaging: Health-information sharing, specifically of digital pathology, is the subject of this paper which discusses how sharing the rich images in pathology can stretch the capabilities of all otherwise well-practiced disciplines. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2012; 100:991-1003. [PMID: 25328166 PMCID: PMC4197933 DOI: 10.1109/jproc.2011.2182074] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Pathology is a medical subspecialty that practices the diagnosis of disease. Microscopic examination of tissue reveals information enabling the pathologist to render accurate diagnoses and to guide therapy. The basic process by which anatomic pathologists render diagnoses has remained relatively unchanged over the last century, yet advances in information technology now offer significant opportunities in image-based diagnostic and research applications. Pathology has lagged behind other healthcare practices such as radiology where digital adoption is widespread. As devices that generate whole slide images become more practical and affordable, practices will increasingly adopt this technology and eventually produce an explosion of data that will quickly eclipse the already vast quantities of radiology imaging data. These advances are accompanied by significant challenges for data management and storage, but they also introduce new opportunities to improve patient care by streamlining and standardizing diagnostic approaches and uncovering disease mechanisms. Computer-based image analysis is already available in commercial diagnostic systems, but further advances in image analysis algorithms are warranted in order to fully realize the benefits of digital pathology in medical discovery and patient care. In coming decades, pathology image analysis will extend beyond the streamlining of diagnostic workflows and minimizing interobserver variability and will begin to provide diagnostic assistance, identify therapeutic targets, and predict patient outcomes and therapeutic responses.
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Affiliation(s)
- Lee A. D. Cooper
- Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA
| | - Alexis B. Carter
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30306 USA
| | - Alton B. Farris
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30306 USA
| | - Fusheng Wang
- Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA
| | - Jun Kong
- Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA
| | - David A. Gutman
- Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA
| | - Patrick Widener
- Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA
| | - Tony C. Pan
- Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA
| | - Sharath R. Cholleti
- Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA
| | - Ashish Sharma
- Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA
| | - Tahsin M. Kurc
- Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA
| | - Daniel J. Brat
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30306 USA
| | - Joel H. Saltz
- Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA
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14
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Levy BP, Hipp JD, Balis UJ, Yagi Y. Potential Applications of Digital Pathology and Image Analysis for Forensic Pathology. Acad Forensic Pathol 2012. [DOI: 10.23907/2012.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
The analysis of digital images using computer algorithms to mine digital data directly is an important and increasingly utilized tool in many different fields of medicine. However, despite the clear value of image analysis to forensics, there is little research and no current practical applications of this technology to forensic pathology. This paper demonstrates the technique of Spatially Invariant Vector Quantization (SIVQ) in the analysis of gross forensic images. SIVQ is a pattern recognition algorithm that can run on a laptop and be utilized by a forensic pathologist with no programming experience. The user merely points and clicks to the feature of interest and SIVQ searches the entire image for the desired feature. The results can be displayed in a variety of formats, both qualitative and quantitative. Gross forensic images from gunshot wounds, burns and patterned contusions were analyzed by a forensic pathologist using SIVQ. Based on features selected by the user, the algorithm was able to detect both the gunshot wound and its marginal abrasion, calculate the surface area of a gunshot wound defect, distinguish between a thermal burn and the surrounding areas of healing, analyze a patterned contusion, and both identify and calculate the density of gunpowder stippling. Even though the work is preliminary, it is obvious from these few examples that SIVQ is easily able to identify features of interest in gross forensic images. Additional work needs to be done to fully explore the potential for this application of technology to the practice of forensic pathology.
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Affiliation(s)
- Bruce P. Levy
- Massachusetts General Hospital and a Clinical Instructor at Harvard Medical School
- Department of Pathology, University of Michigan Health System, Ann Arbor, MI (JH, UB), Department of Pathology, Massachusetts General Hospital, Boston, MA (YY)
| | - Jason D. Hipp
- Department of Pathology, University of Michigan Health System, Ann Arbor, MI (JH, UB), Department of Pathology, Massachusetts General Hospital, Boston, MA (YY)
| | - Ulysses J. Balis
- Department of Pathology, University of Michigan Health System, Ann Arbor, MI (JH, UB), Department of Pathology, Massachusetts General Hospital, Boston, MA (YY)
| | - Yukako Yagi
- Department of Pathology, University of Michigan Health System, Ann Arbor, MI (JH, UB), Department of Pathology, Massachusetts General Hospital, Boston, MA (YY)
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15
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Hipp J, Cheng J, Pantanowitz L, Hewitt S, Yagi Y, Monaco J, Madabhushi A, Rodriguez-Canales J, Hanson J, Roy-Chowdhuri S, Filie AC, Feldman MD, Tomaszewski JE, Shih NN, Brodsky V, Giaccone G, Emmert-Buck MR, Balis UJ. Image microarrays (IMA): Digital pathology's missing tool. J Pathol Inform 2011; 2:47. [PMID: 22200030 PMCID: PMC3237063 DOI: 10.4103/2153-3539.86829] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2011] [Accepted: 09/23/2011] [Indexed: 12/04/2022] Open
Abstract
Introduction: The increasing availability of whole slide imaging (WSI) data sets (digital slides) from glass slides offers new opportunities for the development of computer-aided diagnostic (CAD) algorithms. With the all-digital pathology workflow that these data sets will enable in the near future, literally millions of digital slides will be generated and stored. Consequently, the field in general and pathologists, specifically, will need tools to help extract actionable information from this new and vast collective repository. Methods: To address this limitation, we designed and implemented a tool (dCORE) to enable the systematic capture of image tiles with constrained size and resolution that contain desired histopathologic features. Results: In this communication, we describe a user-friendly tool that will enable pathologists to mine digital slides archives to create image microarrays (IMAs). IMAs are to digital slides as tissue microarrays (TMAs) are to cell blocks. Thus, a single digital slide could be transformed into an array of hundreds to thousands of high quality digital images, with each containing key diagnostic morphologies and appropriate controls. Current manual digital image cut-and-paste methods that allow for the creation of a grid of images (such as an IMA) of matching resolutions are tedious. Conclusion: The ability to create IMAs representing hundreds to thousands of vetted morphologic features has numerous applications in education, proficiency testing, consensus case review, and research. Lastly, in a manner analogous to the way conventional TMA technology has significantly accelerated in situ studies of tissue specimens use of IMAs has similar potential to significantly accelerate CAD algorithm development.
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Affiliation(s)
- Jason Hipp
- Department of Pathology, University of Michigan, M4233A Medical Science I, 1301 Catherine, Ann Arbor, Michigan 48109-0602
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16
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Cheng J, Hipp J, Monaco J, Lucas DR, Madabhushi A, Balis UJ. Automated vector selection of SIVQ and parallel computing integration MATLAB™: Innovations supporting large-scale and high-throughput image analysis studies. J Pathol Inform 2011; 2:37. [PMID: 21886893 PMCID: PMC3162746 DOI: 10.4103/2153-3539.83752] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2011] [Accepted: 06/27/2011] [Indexed: 11/05/2022] Open
Abstract
Introduction: Spatially invariant vector quantization (SIVQ) is a texture and color-based image matching algorithm that queries the image space through the use of ring vectors. In prior studies, the selection of one or more optimal vectors for a particular feature of interest required a manual process, with the user initially stochastically selecting candidate vectors and subsequently testing them upon other regions of the image to verify the vector's sensitivity and specificity properties (typically by reviewing a resultant heat map). In carrying out the prior efforts, the SIVQ algorithm was noted to exhibit highly scalable computational properties, where each region of analysis can take place independently of others, making a compelling case for the exploration of its deployment on high-throughput computing platforms, with the hypothesis that such an exercise will result in performance gains that scale linearly with increasing processor count. Methods: An automated process was developed for the selection of optimal ring vectors to serve as the predicate matching operator in defining histopathological features of interest. Briefly, candidate vectors were generated from every possible coordinate origin within a user-defined vector selection area (VSA) and subsequently compared against user-identified positive and negative “ground truth” regions on the same image. Each vector from the VSA was assessed for its goodness-of-fit to both the positive and negative areas via the use of the receiver operating characteristic (ROC) transfer function, with each assessment resulting in an associated area-under-the-curve (AUC) figure of merit. Results: Use of the above-mentioned automated vector selection process was demonstrated in two cases of use: First, to identify malignant colonic epithelium, and second, to identify soft tissue sarcoma. For both examples, a very satisfactory optimized vector was identified, as defined by the AUC metric. Finally, as an additional effort directed towards attaining high-throughput capability for the SIVQ algorithm, we demonstrated the successful incorporation of it with the MATrix LABoratory (MATLAB™) application interface. Conclusion: The SIVQ algorithm is suitable for automated vector selection settings and high throughput computation.
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Affiliation(s)
- Jerome Cheng
- Department of Pathology, University of Michigan Health System, M4233A Medical Science I, 1301 Catherine, Ann Arbor, Michigan 48109-0602
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