Biology subjects

Chang, Y. H.

Publications and source records attributed to Chang, Y. H..

At least 19 recordsLinked to original sources

bioRxiv2026

Deep learning-based identification and quantification of rare circulating hybrid cells in orthotopic pancreatic cancer models

SignificanceRare-cell identification in fluorescence microscopy remains challenging because targets are sparse and background varies between specimens. Combining specimen-specific fluorescence enrichment with image classification may enable efficient and more specific automated detection of rare cells. AimWe developed a two-stage framework to identify and quantify candidate rare circulating hybrid neoplastic cells (CHCs, ECAD+/CD45+) in peripheral blood mononuclear cell (PBMC) preparations from tumor-bearing and tumor-naive mice. ApproachPBMCs from 28 mice were imaged by multichannel fluorescence microscopy. Matched unstained samples established animal-specific ECAD and CD45 background distributions for candidate cell enrichment. Blinded multi-annotator consensus labels were used to train a convolutional neural network (CNN) from DAPI, ECAD, and CD45 image crops. Generalization was evaluated by leave-one-animal-out validation across 10 random initializations. Final classification used a 10-model ensemble, and rare-cell burden was compared between groups using negative-binomial regression with total segmented-cell count as an exposure. ResultsOf the 1,065,512 segmented cells, enrichment retained 10,176 candidates (0.96%), reducing the search space by >99%. Four of five evaluable tumor-bearing animals showed reproducible held-out discrimination, with median quantified area under the receiver operator characteristic curve (AUROCs) of 0.918-0.951; one animal was a reproducible outlier (median AUROC, 0.338). Ensemble deployment identified 157.94 positive-consensus cells per 50,000 segmented cells in tumor-bearing animals versus 49.55 in controls. The estimated rare-cell rate was 3.15-fold higher in tumor-bearing animals (95% CI, 0.91-10.99; two-sided p=0.071; prespecified one-sided p=0.036). ConclusionsSpecimen-specific fluorescence enrichment combined with supervised image classification reduced the cellular search space and enabled automated quantification of a rare CHC (ECAD+/CD45+) phenotypes. Cross-animal validation also identified specimen-specific generalization failure, highlighting the importance of biological-specimen-level validation.

cancer biology↗

bioRxiv2026

Spatially resolved transcriptomic and proteomic profiling reveals cell interaction programs that predict Barrett's esophagus progression

Barretts esophagus (BE) is the precursor lesion of esophageal adenocarcinoma (EAC). It affects approximately 5% of adults in the United States and significantly increases the risk of developing EAC. However, current surveillance strategies cannot reliably distinguish patients who will progress from those who will remain stable. Direct studies of progressor BE are extremely limited due to availability of tissue with known progression outcomes, and have largely been restricted to genomic profiling approaches. The premalignant cellular landscape of progressor BE remains poorly understood. Here, we used complementary spatial transcriptomic and proteomic imaging to profile 34 non-dysplastic BE patients under endoscopic surveillance, including those who subsequently progressed to dysplasia or EAC, termed "Progressors" and those who remained stable, termed "Non-progressors". Transcriptomics based Xenium analysis captured 974,604 cells across 70 whole-biopsy regions, while protein based imaging mass cytometry profiled 372,242 cells across 119 selected regions. FUME-TCRseq further quantified T cell clonotypes from matched tissues scrolls. Cellular composition was generally similar between Progressors and Non-progressors. However, Progressors showed increased intestinal Barretts columnar cells, B cells and gastric progenitor-like cells, together with enhanced immune-epithelial interactions, whereas Non-progressors retained coordinated stromal organization. Spatial interaction features strongly outperformed cell composition and density for progression prediction. Combined spatial interaction model achieved an area under the curve (AUC) of 0.97, compared with 0.62 and 0.68 for comparison and density alone. Complementary imaging mass cytometry further resolved the underlying immune programs, identifying cytotoxic and antigen presenting myeloid features enriched in progressors, and CD56 associated memory T cell interactions enriched in non progressors. Together, these findings support a model that BE progression is driven by progressive remodeling of epithelial-immune-stromal architecture rather than emergence of distinct dysplasia-like cell subsets. Increased T cell clonal diversity and recruitment of cytotoxic and antigen-presenting immune niches may also reflect an evolving response to genomic alteration prior to dysplasia. These results establish spatial tissue architecture, rather than specific cell types, captures progression associated microenvironmental states in BE and provides a framework for spatially informed patient stratification and early cancer risk assessment.

systems biology↗

bioRxiv2026

Cell fusion reprograms tumor cells and promotes RUNX1-mediated invasion and dissemination in colorectal cancer

Metastasis remains the primary cause of cancer-related morbidity and mortality, despite significant advances in targeted therapies. Although metastatic dissemination requires tumor cells to escape the primary lesion and colonize distant organs, the mechanisms by which primary tumor cells gain metastatic competence remain poorly understood. Increasing evidence demonstrates that fusion of tumor (i.e., neoplastic) and immune (e.g., macrophages) cells generate a distinct population of tumor-immune hybrid cells with enhanced functional ability to migrate and disseminate into peripheral blood. Herein, our study investigates tumor-macrophage hybrid cells, an underexplored population of disseminated tumor cells, and their inherent heterogeneity and acquisition of molecular mechanisms underlying their dissemination as metastatic effectors in colorectal cancer (CRC). Through hybrid cell phenotyping utilizing integrative single-cell RNA sequencing (scRNA-seq), cyclic immunofluorescence (cyCIF) and functional assays with an in vitro model of CRC hybrid cells, we identify Runt-related transcription factor 1 (Runx1) as a central regulator of hybrid cell motility and invasion. Runx1 depletion in hybrid cells suppressed functional protease expression, chemotactic activity and extracellular matrix (ECM) invasion. Furthermore, pharmacologic inhibition of RUNX1 in an in vivo model reduced hybrid tumor growth and dissemination into peripheral blood, key attributes of metastatic spread of disease. In patients with CRC, RUNX1+ hybrid cells were identified in both primary tumor and peripheral blood, where circulating hybrid cells (CHCs) exhibited enriched migratory and epithelial-to-mesenchymal transition (EMT) phenotypes. Taken together, these findings reveal a mechanistic role for RUNX1 in driving invasive behavior of tumor-immune hybrids and highlight disseminated CHCs as an under-recognized contributor to metastatic spread and a promising noninvasive biomarker for tumor progression.

cancer biology↗

bioRxiv2026

Tumor-immune trajectory context connects static tissue architecture to clinical outcomes

Multiplexed tissue imaging (MTI) has revealed recurrent tumor microenvironment (TME) architectures with prognostic value, yet these measurements are inherently static, obscuring dynamic changes in the TME that govern therapeutic response. Here, we introduce a trajectory-centric framework that reconstructs continuous TME dynamics by integrating agent-based mathematical modeling and simulation with state space analysis. This approach yields a mechanistically constrained reference landscape built entirely from in silico simulation, and onto which static patient biospecimens can be projected and mapped onto simulated TME trajectories. Systematic simulation of tumor-immune interactions in triple-negative breast cancer identifies six metastable TME states connected by transition pathways spanning immune control to immune escape. Mapping MTI data from two independent patient cohorts, including longitudinal samples from a randomized immunotherapy trial, validates this landscape by positioning individual biospecimens along inferred TME trajectories rather than in static states. We show that treatment-phase TME states, but not pre-treatment configurations, robustly predict immunotherapy response, and identical terminal states can arise from distinct trajectory histories corresponding to immune failure or resolved inflammation. Thus, this framework enables mechanistic simulations to define a reference dynamical landscape that serves as a coordinate system for interpreting static clinical spatial data, providing a principled basis for evaluating consistency, predictiveness, and clinical relevance across independent patient cohorts. Altogether, this study advances spatial tumor profiling from static state classification of human tissues to dynamic trajectory inference, establishing a quantitative framework for trajectory-informed, state-guided, and temporally adaptive immunotherapy strategies.

cancer biology↗

bioRxiv2026

miniMTI: minimal multiplex tissue imaging enhances biomarker expression prediction from histology

Virtual multiplexing from routine histology has advanced rapidly, yet morphology alone provides limited access to molecular state, imposing an intrinsic ceiling on H&E-only inference. Here, we introduce miniMTI, a molecularly anchored virtual staining framework that determines the minimal set of experimentally measured markers required, alongside H&E, to accurately reconstruct large multiplex tissue imaging (MTI) panels while preserving biologically and clinically relevant information. miniMTI learns from paired same-section H&E-MTI data using a unified multimodal generative model that can condition on arbitrary combinations of measured marker channels, coupled with an iterative panel selection strategy to rank informative molecular anchors. Across colorectal and prostate cancer cohorts spanning two MTI platforms and over 40 million cells, miniMTI reduces a 40-marker MTI assay to H&E plus as few as three measured molecular markers, while accurately recovering withheld markers, preserving cellular phenotypes and spatial tissue architecture, and disease-associated molecular programs, including Gleason grade-linked signatures. By integrating histology context with sparse molecular grounding, miniMTI overcomes the limitations of morphology-only virtual staining and provides a scalable, cost-effective approach for expanding MTI-level biomarker coverage with retained biological interpretability and clinical relevance.

bioinformatics↗

bioRxiv2025

Automated Quality Control of Time-Course Imaging from 3D in vitro cultures

Longitudinal imaging of 3D cell cultures like tumor organoids and spheroids offers crucial insights into cancer progression and treatment. However, spatial displacement during time-course imaging, caused by matrix detachment or experimental artifacts, can confound analyses. Existing computational methods struggle to address this issue. We present a new algorithm to evaluate data integrity and rectify mislabeling in longitudinal imaging of 3D cell culture. Our algorithm integrates permutation-based optimization with Procrustes analysis. By using X and Y coordinates of images, it accurately reorders, matches, and aligns object positions across time points, correcting for rotation, translation, and small movements. Validation with simulated data confirmed its accuracy and robustness. Applied to longitudinal imaging of tumor spheroids, our algorithm revealed frequent displacement amongst the spheroids between time points and corrected many mislabeled images. This computationally efficient and adaptable method needs no experimental adjustments and presents a readily accessible solution for data quality control. MotivationThree-dimensional (3D) in vitro models, such as tumor organoids and spheroids embedded in an extracellular matrix, are increasingly vital for studying normal and disease biology, including drug responses.1-3 A key advantage of these models is that imaging platforms can perform continuous longitudinal imaging to track phenotypic changes. However, common issues in 3D techniques, such as matrix shifts during experimental setup or image capture, can introduce technical artifacts that affect downstream analyses. Currently, no automated analytical approaches exist for assessing or correcting technical artifacts. Here, we introduce a robust, automated algorithm for assessing the quality of time-course image data and, in some cases, correcting object mislabeling to enable accurate tracking of individual spheroids over time. This approach relies only on image metadata, requiring no experimental modifications. It offers a readily implementable solution for improving data integrity and reproducibility and enhancing the reliability of longitudinal 3D cell culture studies.

bioengineering↗

bioRxiv2025

Language of Stains: Tokenization Enhances Multiplex Immunofluorescence and Histology Image Synthesis

Multiplex tissue imaging (MTI) is a powerful tool in cancer research, allowing spatially resolved, single-cell phenotype analysis. However, MTI platforms face challenges such as high costs, tissue loss, lengthy acquisition times, and complex analysis of large, multichannel images with batch effects. To address these challenges, we propose a novel computational method to model the interactions between dozens of panel markers and Hematoxylin & Eosin (H&E) staining, enabling in-silico generation of marker stains. This approach reduces the reliance on experimentally measured markers, bridging low-cost H&E data with MTIs high-content information. Our approach uses a two-stage frame-work for channel-wise bioimage synthesis: first, vector quantization learns a visual token vocabulary, then a bidirectional transformer infers missing markers through masked language modeling. Comprehensive bench-marking across different MTI platforms and tissue types demonstrates the effectiveness of our method in improving marker prediction while maintaining biological relevance. This advance makes high-dimensional multiplex tissue imaging more accessible and scalable, supporting deeper insights and potential clinical applications in cancer research.

bioinformatics↗

bioRxiv2024

UniFORM: Towards Universal Immunofluorescence Normalization for Multiplex Tissue Imaging

Multiplexed tissue imaging (MTI) technologies enable high-dimensional spatial analysis of tumor microenvironments but face challenges with technical variability in staining intensities. Existing normalization methods, including Z-score, ComBat, and MxNorm, often fail to account for the heterogeneous, right-skewed expression patterns of MTI data, compromising signal alignment and downstream analyses. We present UniFORM, a non-parametric, Python-based pipeline that uses an automated rigid landmark functional data registration approach for normalizing both feature- and pixel-level MTI data. Designed specifically for the distributional characteristics of MTI datasets, UniFORM operates without prior distributional assumptions and performs robustly regardless of distribution modality, including both unimodal and bimodal patterns. It removes technical variation by aligning the biologically invariant component of the signal, typically the negative (non-expressing) population, while preserving biologically meaningful variation in the positive population, thereby maintaining tissue-specific expression patterns essential for downstream analysis. Benchmarking across three distinct MTI platform datasets demonstrates that UniFORM outperforms existing methods in mitigating batch effects while maintaining biological signal fidelity. This is evidenced by improved marker distribution alignment and positive population preservation, enhanced kBET and Silhouette scores, and improved downstream analyses such as UMAP visualizations and Leiden clustering. UniFORM also introduces a novel guided fine-tuning option for complex and heterogeneous datasets. Although optimized for fluorescence-based platforms, UniFORM provides a scalable and robust solution for MTI data normalization, enabling accurate and biologically meaningful interpretations.

bioinformatics↗

bioRxiv2024

AxonFinder: Automated segmentation of tumor innervating neuronal fibers

Neurosignaling is increasingly recognized as a critical factor in cancer progression, where neuronal innervation of primary tumors contributes to the diseases advancement. This study focuses on segmenting individual axons within the prostate tumor microenvironment, which have been challenging to detect and analyze due to their irregular morphologies. We present a novel deep learning-based approach for the automated segmentation of axons, AxonFinder, leveraging a U-Net model with a ResNet-101 encoder, based on a multiplexed imaging approach. Utilizing a dataset of whole-slide images from low-, intermediate-, and high-risk prostate cancer patients, we manually annotated axons to train our model, achieving significant accuracy in detecting axonal structures that were previously hard to segment. Our analysis includes a comprehensive assessment of axon density and morphological features across different CAPRA-S prostate cancer risk categories, providing insights into the correlation between tumor innervation and cancer progression. Our paper suggests the potential utility of neuronal markers in the prognostic assessment of prostate cancer in aiding the pathologists assessment of tumor sections and advancing our understanding of neurosignaling in the tumor microenvironment.

systems biology↗

bioRxiv2024

COEXIST: Coordinated single-cell integration of serial multiplexed tissue images

Multiplexed tissue imaging (MTI) and other spatial profiling technologies commonly utilize serial tissue sectioning to comprehensively profile samples by imaging each section with unique biomarker panels or assays. The dependence on serial sections is attributed to technological limitations of MTI panel size or incompatible multi-assay protocols. Although image registration can align serially sectioned MTIs, integration at the single-cell level poses a challenge due to inherent biological heterogeneity. Existing computational methods overlook both cell population heterogeneity across modalities and spatial information, which are critical for effectively completing this task. To address this problem, we first use Monte-Carlo simulations to estimate the overlap between serial 5m-thick sections. We then introduce COEXIST, a novel algorithm that synergistically combines shared molecular profiles with spatial information to seamlessly integrate serial sections at the single-cell level. We demonstrate COEXIST necessity and performance across several applications. These include combining MTI panels for improved spatial single-cell profiling, rectification of miscalled cell phenotypes using a single MTI panel, and the comparison of MTI platforms at single-cell resolution. COEXIST not only elevates MTI platform validation but also overcomes the constraints of MTIs panel size and the limitation of full nuclei on a single slide, capturing more intact nuclei in consecutive sections and thus enabling deeper profiling of cell lineages and functional states.

bioinformatics↗

bioRxiv2024

Unraveling the Complexity of Abdominal Aortic Aneurysm: Multiplexed Imaging Insights into C-Reactive Protein-Related Variations

BackgroundAbdominal aortic aneurysm (AAA) is a potentially lethal condition that often remains asymptomatic until it ruptures. Recent research suggests that immune-inflammatory processes are associated with AAA development, yet the exact mechanisms remain unclear. Serum C-reactive protein (CRP) serves as a prognostic marker for AAA and various cardiovascular diseases. When CRP accumulates in damaged tissues, it transforms into a monomeric form, exacerbating tissue damage. Our previous study confirmed the presence of CRP deposition in eroded AAA and atherosclerosis regions, accompanied by an increased infiltration of inflammatory cells. However, a comprehensive understanding of the specific changes in the inflammatory cellular landscape attributable to CRP deposition is lacking. Here, we aimed to explore cellular-level alterations in AAAs associated with varying CRP levels. MethodsWe categorized AAA patients into High-CRP ([≥]0.1 mg/dL, and [≥]3+ CRP IHC score, n=6) and Low-CRP ([≤]0.1 mg/dL, and [≤]1+ CRP IHC score, n=3), and used normal aorta specimens as a baseline control. The cellular landscape of immune and stromal components was characterized using Co-Detection by Indexing (CODEX) tissue imaging with 31 DNA-barcoded antibodies, followed by single-cell-based analysis and GPU-accelerated unsupervised clustering. ResultsWe identified 51 distinct immune and stromal cell types in the cohort and revealed significant differences in protein expression patterns among the groups. In AAA, stromal cells decreased significantly, while immune cell proportions sharply increased. Lag3+ T cell regulators decreased, leading to an increase in CD3+ T cells. The composition of immune cells within atherosclerotic plaques was associated with the degree of CRP deposition in AAA. The High-CRP group showed increased M1 and Ki67+ proliferating macrophages, while the Low-CRP group exhibited intensified fibrosis with M2 macrophages. ConclusionsOur study found significant variations in immune cell distribution within AAA walls based on CRP levels. These findings suggest a potential link between CRP-related immune changes and AAA progression. HIGHLIGHTSO_LIBy performing CODEX (co-detection by indexing) multiplexed imaging on paraffin-embedded, formalin-fixed archived abdominal aortic aneurysm (AAA) tissue samples with varying CRP levels, we segmented 415,365 cells into 51 distinct clusters. C_LIO_LIIn AAA, the presence of CRP deposition led to variations in the spatial relationships, distances, and enrichment patterns among immune and stromal cells.. C_LIO_LIIn AAA-High CRP, there was an intensified infiltration of M1 macrophages and various immune cells observed in the atherosclerotic plaque. C_LIO_LIIn AAA-Low CRP, severe stromal fibrosis was associated with M2 macrophages. C_LIO_LIOur results suggest that the deposition of CRP into the atherosclerotic plaque alters the immune-stromal landscape in AAA. C_LI

pathology↗

bioRxiv2024

Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change

Extracellular signals induce changes to molecular programs that modulate multiple cellular phenotypes, including proliferation, motility, and differentiation status. The connection between dynamically adapting phenotypic states and the molecular programs that define them is not well understood. Here we develop data-driven models of single-cell phenotypic responses to extracellular stimuli by linking gene transcription levels to "morphodynamics" - changes in cell morphology and motility observable in time-lapse image data. We adopt a dynamics-first view of cell state by grouping single-cell trajectories into states with shared morphodynamic responses. The single-cell trajectories enable development of a first-of-its-kind computational approach to map live-cell dynamics to snapshot gene transcript levels, which we term MMIST, Molecular and Morphodynamics-Integrated Single-cell Trajectories. The key conceptual advance of MMIST is that cell behavior can be quantified based on dynamically defined states and that extracellular signals change the overall distribution of cell states by altering rates of switching between states. We find a cell state landscape that is bound by epithelial and mesenchymal endpoints, with distinct sequences of epithelial to mesenchymal transition (EMT) and mesenchymal to epithelial transition (MET) intermediates. The analysis yields predictions for gene expression changes consistent with curated EMT gene sets and predicts expression of thousands of RNA transcripts through extracellular signal-induced EMT and MET with near-continuous time resolution. The MMIST framework leverages true single-cell dynamical behavior to generate molecular-level omics inferences and is broadly applicable to other biological domains, time-lapse imaging approaches and molecular snapshot data. SummaryIn normal homeostatic tissues, extracellular signals induce changes in the behavior and state of epithelial cells, and aberrant responses to such signals are associated with diseases. To decode and potentially steer these responses, it is essential to link live-cell behavior to molecular programs; however, high-throughput molecular techniques are destructive or require fixation. Here we present a novel computational approach to connect single-cell measures of cell phenotype and behavior to bulk molecular readouts, enabling prediction of dynamic changes in gene expression programs. This reveals molecular programs associated with distinct cell states and identifies drivers that may be manipulated to control cell state change.

cell biology↗

bioRxiv2023

The changing morphology of the ventricular walls of mouse and human with increasing gestation.

That the highly trabeculated ventricular walls of the developing embryos transforms to the arrangement during the fetal stages, when the mural architecture is dominated by the thickness of the compact myocardium, has been explained by coalescence of trabeculations, often erroneously described as "compaction". Recent data, however, supports differential rates of growth of the trabecular and compact layers as the major driver of change. Here, these processes were assessed quantitatively and morphologically using a larger dataset than has previously been available of mouse hearts from embryonic day 10.5 to postnatal day 3, supported by images from human hearts. The volume of the trabecular layer increased throughout development, in contrast to what would be expected had there been "compaction". During the embryonic-fetal transition, fast growth of the compact layer diminished the proportion of trabeculations. Similarly, great expansion of the central cavity reduced the proportion that intertrabecular recesses make of the total cavity. Using the median value of left ventricular trabeculation, we provided illustrations for each gestational day so as to provide pictorial evidence of the changes. The illustrations confirmed a pronounced growth of the compact wall, and prominence of the central cavity. This corresponds, in morphological terms, to a reduction in the extent of the trabecular layer. Similar observations were made in the human hearts. We conclude that it is a period of comparatively slow growth of the trabecular layer, rather than so-called compaction, that is the major determinant of the changing morphology of the ventricular walls of both mouse and human.

developmental biology↗

bioRxiv2023

Longitudinal and multimodal auditing of tumor adaptation to CDK4/6 inhibitors in HR+ metastatic breast cancers

CDK4/6 inhibitors (CDK4/6i) have transformed the treatment of hormone receptor-positive (HR+), HER2-negative (HR+) breast cancers as they are effective across all clinicopathological, age, and ethnicity subgroups for metastatic HR+ breast cancer. In metastatic ER+ breast cancer, CDK4/6i lead to strong and consistent improvement in survival across different lines of therapy. To improve understanding of how metastatic HR+ breast cancers become refractory to CDK4/6i, we have created a multimodal and longitudinal tumor atlas to investigate therapeutic adaptations in malignant cells and in the tumor immune microenvironment. This atlas is part of the NCI Cancer Moonshot Human Tumor Atlas Network and includes seven pairs of pre- and on-progression biopsies from five metastatic HR+ breast cancer patients treated with CDK4/6i. Biopsies were profiled with bulk genomics, transcriptomics, and proteomics as well as single-cell ATAC-seq and multiplex tissue imaging for spatial, single-cell resolution. These molecular datasets were then linked with detailed clinical metadata to create an atlas for understanding tumor adaptations during therapy. Analysis of our atlas datasets suggests a diverse set of tumor adaptations to CDK4/6i therapy. Malignant cells may adapt to therapy via mTORC1 activation, cell cycle bypass, and increased replication stress. The tumor immune microenvironment displayed evidence of both immune activation and immune suppression during therapy. Together, our metastatic ER+ breast cancer atlas represents a rich multimodal resource to better understand HR+ breast cancer tumor therapeutic adaptations to CDK4/6i therapy.

cancer biology↗

bioRxiv2023

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework--a cell behavior hypothesis grammar--that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.

systems biology↗

bioRxiv2023

Quantitative image analysis pipeline for detecting circulating hybrid cells in immunofluorescence images with human-level accuracy

Circulating hybrid cells (CHCs) are a newly discovered, tumor-derived cell population identified in the peripheral blood of cancer patients and are thought to contribute to tumor metastasis. However, identifying CHCs by immunofluorescence (IF) imaging of patient peripheral blood mononuclear cells (PBMCs) is a time-consuming and subjective process that currently relies on manual annotation by laboratory technicians. Additionally, while IF is relatively easy to apply to tissue sections, its application on PBMC smears presents challenges due to the presence of biological and technical artifacts. To address these challenges, we present a robust image analysis pipeline to automate the detection and analyses of CHCs in IF images. The pipeline incorporates quality control to optimize specimen preparation protocols and remove unwanted artifacts, leverages a {beta}-variational autoencoder (VAE) to learn meaningful latent representations of single-cell images and employs a support vector machine (SVM) classifier to achieve human-level CHC detection. We created a rigorously labeled IF CHC dataset including 9 patients and 2 disease sites with the assistance of 10 annotators to evaluate the pipeline. We examined annotator variation and bias in CHC detection and then provided guidelines to optimize the accuracy of CHC annotation. We found that all annotators agreed on CHC identification for only 65% of the cells in the dataset and had a tendency to underestimate CHC counts for regions of interest (ROI) containing relatively large amounts of cells (>50,000) when using conventional enumeration methods. On the other hand, our proposed approach is unbiased to ROI size. The SVM classifier trained on the {beta}-VAE encodings achieved an F1 score of 0.80, matching the average performance of annotators. Our pipeline enables researchers to explore the role of CHCs in cancer progression and assess their potential as a clinical biomarker for metastasis. Further, we demonstrate that the pipeline can identify discrete cellular phenotypes among PBMCs, highlighting its utility beyond CHCs.

bioinformatics↗

bioRxiv2023

Multiplex imaging of localized prostate tumors reveals changes in mast cell type composition and spatial organization of AR-positive cells in the tumor microenvironment

Mapping spatial interactions of cancer, immune and stromal cells present novel opportunities for patient stratification and for advancing immunotherapy. While single-cell studies revealed significant molecular heterogeneity in prostate tumors, there is currently no understanding of how immune cell heterogeneity impacts spatial coordination between tumor and stromal cells in localized tumors. Here, we used cyclic immunofluorescent imaging on whole-tissue sections to uncover novel spatial associations between cancer and stromal cells in low- and high-grade prostate tumors and tumor-adjacent normal tissues. Our results provide a spatial map of 699,461 single-cells that show epigenetic and molecular differences in distinct clinical grades. We report unique populations of mast cells that differentially express CD44, CD90 and Granzyme B (GZMB) and demonstrate GZMB+ mast cells are spatially associated with M2 macrophages in prostate tumors. Finally, we uncover recurrent neighborhoods that are primarily driven by androgen receptor positive (AR+) stromal cells and identify transcriptional networks active in AR+ prostate stroma.

cancer biology↗

bioRxiv2023

Addressing persistent challenges in digital image analysis of cancerous tissues

The National Cancer Institute (NCI) supports many research programs and consortia, many of which use imaging as a major modality for characterizing cancerous tissue. A trans-consortia Image Analysis Working Group (IAWG) was established in 2019 with a mission to disseminate imaging-related work and foster collaborations. In 2022, the IAWG held a virtual hackathon focused on addressing challenges of analyzing high dimensional datasets from fixed cancerous tissues. Standard image processing techniques have automated feature extraction, but the next generation of imaging data requires more advanced methods to fully utilize the available information. In this perspective, we discuss current limitations of the automated analysis of multiplexed tissue images, the first steps toward deeper understanding of these limitations, what possible solutions have been developed, any new or refined approaches that were developed during the Image Analysis Hackathon 2022, and where further effort is required. The outstanding problems addressed in the hackathon fell into three main themes: 1) challenges to cell type classification and assessment, 2) translation and visual representation of spatial aspects of high dimensional data, and 3) scaling digital image analyses to large (multi-TB) datasets. We describe the rationale for each specific challenge and the progress made toward addressing it during the hackathon. We also suggest areas that would benefit from more focus and offer insight into broader challenges that the community will need to address as new technologies are developed and integrated into the broad range of image-based modalities and analytical resources already in use within the cancer research community.

bioinformatics↗