Heterogeneous Cell Research Articles

Arming Vδ2 T cells with chimeric antigen receptors to combat cancer.

Immunotherapy has emerged as a promising approach in the field of cancer treatment, with chimeric antigen receptor (CAR) T cell therapy demonstrating remarkable success. However, challenges such as tumor antigen heterogeneity, immune evasion, and limited persistence of CAR-T cells have prompted the exploration of alternative cell types for CAR-based strategies. Gamma delta T cells, a unique subset of lymphocytes with inherent tumor recognition capabilities and versatile immune functions, have garnered increasing attention in recent years. In this review, we will present how arming Vδ2-T cells might be the basis for next-generation immunotherapies against solid tumors. Following a comprehensive overview of γδ T cell biology and innovative CAR engineering strategies, we will discuss the clinical potential of Vδ2 CAR-T cells to overcome the current limitations of immunotherapy in solid tumors. Indeed, while applications of Vδ2 CAR-T cells in cancer research are relatively in their infancy and many challenges are yet to be identified, Vδ2 CAR-T cells represent a promising breakthrough in cancer immunotherapy.

Quantitative systems pharmacology modeling of tumor heterogeneity in response to BH3-mimetics using virtual tumors calibrated with cell viability assays.

Both primary and acquired resistance mechanisms that involve intra-tumoral cell heterogeneity limit the use of BH3-mimetics to trigger tumor cell apoptosis. This article proposes a new quantitative systems pharmacology (QSP)-based methodology in which cell viability assays are used to calibrate virtual tumors (VTs) made of virtual cells whose fate is determined by simulations from an apoptosis QSP model. VTs representing SU-DHL-4 and KARPAS-422 cell lines were calibrated using invitro data involving venetoclax (anti-BCL2), A-1155463 (anti-BCLXL), and/or A-1210477 (anti-MCL1). The calibrated VTs provide insights into the combination of several BH3-mimetics, such as the distinction between cells eliminated by at least one of the drugs (monotherapies) from the cells eliminated by a pharmacological combination only. Calibrated VTs can also be used as initial conditions in an agent-based model (ABM) framework, and a minimal ABM was developed to bridge invitro SU-DHL-4 cell viability results to tumor growth inhibition experiments in mice.

Network representation of multicellular activity in pancreatic islets: Technical considerations for functional connectivity analysis.

Within the islets of Langerhans, beta cells orchestrate synchronized insulin secretion, a pivotal aspect of metabolic homeostasis. Despite the inherent heterogeneity and multimodal activity of individual cells, intercellular coupling acts as a homogenizing force, enabling coordinated responses through the propagation of intercellular waves. Disruptions in this coordination are implicated in irregular insulin secretion, a hallmark of diabetes. Recently, innovative approaches, such as integrating multicellular calcium imaging with network analysis, have emerged for a quantitative assessment of the cellular activity in islets. However, different groups use distinct experimental preparations, microscopic techniques, apply different methods to process the measured signals and use various methods to derive functional connectivity patterns. This makes comparisons between findings and their integration into a bigger picture difficult and has led to disputes in functional connectivity interpretations. To address these issues, we present here a systematic analysis of how different approaches influence the network representation of islet activity. Our findings show that the choice of methods used to construct networks is not crucial, although care is needed when combining data from different islets. Conversely, the conclusions drawn from network analysis can be heavily affected by the pre-processing of the time series, the type of the oscillatory component in the signals, and by the experimental preparation. Our tutorial-like investigation aims to resolve interpretational issues, reconcile conflicting views, advance functional implications, and encourage researchers to adopt connectivity analysis. As we conclude, we outline challenges for future research, emphasizing the broader applicability of our conclusions to other tissues exhibiting complex multicellular dynamics.

Electrical signature of heterogeneous human mesenchymal stem cells.

Human mesenchymal stem cells (hMSCs) have gained traction in transplantation therapy due to their immunomodulatory, paracrine, immune-evasive, and multipotent differentiation potential. The inherent heterogeneity of hMSCs poses a challenge for therapeutic treatments and necessitates the identification of robust biomarkers to ensure reproducibility in both in vivo and in vitro experiments. In this study, we utilized dielectrophoresis (DEP), a label-free electrokinetic phenomenon, to investigate the heterogeneity of hMSCs derived from bone marrow (BM) and adipose tissue (AD). The electrical properties of BM-hMSCs were compared to homogeneous mouse fibroblasts (NIH-3T3), human fibroblasts (WS1), and human embryonic kidney cells (HEK-293). The DEP profile of BM-hMSCs differed most from HEK-293 cells. We compared the DEP profiles of BM-hMSCs and AD-hMSCs and found that they have similar membrane capacitances, differing cytoplasm conductivity, and transient slopes. Inducing both populations to differentiate into adipocyte and osteoblast cells revealed that they behave differently in response to differentiation-inducing cytokines. Histology and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analyses of the differentiation-related genes revealed differences in heterogeneity between BM-hMSCs and AD-hMSCs. The differentiation profiles correlate well with the DEP profiles developed and indicate differences in the heterogeneity of BM-hMSCs and AD-hMSCs. Our results demonstrate that using DEP, membrane capacitance, cytoplasm conductivity, and transient slope can uniquely characterize the inherent heterogeneity of hMSCs to guide robust and reproducible stem cell transplantation therapies.

CellPLATO: an unsupervised method for identifying cell behaviour in heterogeneous cell trajectory data.

Advances in imaging, segmentation, and tracking have led to the routine generation of large and complex microscopy datasets. New tools are required to process this 'phenomics' type data. Cell PLasticity Analysis TOol (cellPLATO) is a Python-based analysis software designed for measurement and classification of cell behaviours based on clustering features of cell morphology and motility. Used after segmentation and tracking, the tool extracts features from each cell per timepoint, using them to segregate cells into dimensionally reduced behavioural subtypes. Resultant cell tracks describe a 'behavioural ID' at each timepoint and similarity analysis allows the grouping of behavioural sequences into discrete trajectories with assigned IDs. Here, we use cellPLATO to investigate the role of IL-15 in modulating human NK cell migration on ICAM-1 or VCAM-1. We find 8 behavioural subsets of NK cells based on their shape and migration dynamics between single timepoints, and 4 trajectories based on sequences of these behaviours over time. Therefore, using cellPLATO we show that IL-15 increases plasticity between cell migration behaviours and that different integrin ligands induce different forms of NK cell migration.

Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity.

Single-cell epigenomic data has been growing continuously at an unprecedented pace, but their characteristics such as high dimensionality and sparsity pose substantial challenges to downstream analysis. Although deep learning models-especially variational autoencoders-have been widely used to capture low-dimensional feature embeddings, the prevalent Gaussian assumption somewhat disagrees with real data, and these models tend to struggle to incorporate reference information from abundant cell atlases. Here we propose CASTLE, a deep generative model based on the vector-quantized variational autoencoder framework to extract discrete latent embeddings that interpretably characterize single-cell chromatin accessibility sequencing data. We validate the performance and robustness of CASTLE for accurate cell-type identification and reasonable visualization compared with state-of-the-art methods. We demonstrate the advantages of CASTLE for effective incorporation of existing massive reference datasets in a weakly supervised or supervised manner. We further demonstrate CASTLE's capacity for intuitively distilling cell-type-specific feature spectra that unveil cell heterogeneity and biological implications quantitatively.

Circulating tumour DNA analysis and new uses of anti-EGFR monoclonal antibodies in patients with metastatic colorectal cancer

Currently, tumour tissue biopsy to determine RAS/BRAF gene alterations, assess microsatellite instability status, and determine HER‑2/neu gene amplification/hyperexpression is the gold standard of diagnosis and allows the selection of optimal molecularly targeted therapy when considering treatment strategies for patients with metastatic colorectal cancer. However, biopsy does not fully reflect the existing intratumoural heterogeneity and clonal evolution of tumour cells, which can often be the cause of therapeutic failures. In recent years, liquid biopsy has attracted increasing attention as an additional and potentially alternative non-invasive tool for molecular tumour profiling. Assessment of circulating tumour DNA allows changes in the genetic status of the tumour to be monitored and the «burden» of disease to be measured dynamically in real time. Advances in liquid biopsy technology have led to promising new strategies for the management of patients with metastatic colorectal cancer in late-line therapy. The standard drug arsenal in this group of patients is limited to either repeat administration of previously effective therapy or regorafenib and the combination of trifluridine/tipiracil with bevacizumab, which are characterized by limited clinical activity. However, thanks to the discovery of the NeoRAS wild-type phenomenon and the rechallenge strategy of anti-EGFR monoclonal antibodies based on the study of clonal selection and evolution of tumour cells, the administration of epidermal growth factor inhibitors in a molecularly selected by liquid biopsy population is accompanied by good tolerability and efficacy. Numerous clinical studies are ongoing to further understand the mechanisms of tumour resistance and to develop new evidence-based treatment approaches in order to realise the concept of personalised medicine.

Phenotypic heterogeneity of mycosis fungoides cells leads to the difficulties in determining tumour origin.

Mycosis fungoides (MF) is a low-grade malignant cutaneous T-cell lymphoma that originates from memory T cells. It typically follows a unique and relatively indolent disease course. MF is used to be characterized by a tissue-resident memory T cell (TRM) phenotype, although recent molecular research has revealed its complexity, casting doubt on the cell of origin and the TRM-MF paradigm. Recent clonal heterogeneity studies suggest that MF may originate from immature early precursor T cells. During development, the tumour microenvironment (TME) influences tumour cell phenotype. The exact origin and development trajectory of MF remains elusive. Clarifying the origin of MF cells is vital for accurate diagnosis and effective treatment.

Effects of design and manufacturing deviations on compressive properties of glass sponge lattice structures manufactured by selective laser melting

Bio-inspiration offers researchers innovative perspectives and methodologies, stimulating the advancement of high-performance, high-durability, and added-value materials, structures, and products. Inspired by the lightweight, high-strength, and highly stable glass sponges (GSs), five types of glass sponge lattice structures (GSLSs) were successfully designed and manufactured by selective laser melting (SLM) with Ti6Al4V. By conducting a comparative analysis of unit cell design and manufacturing deviations, we assess their impact on the compressive properties of GSLSs. Notably, the original GSLS with circle-/grid-like heterogeneous geometries exhibits outstanding overall compression performance, including stability, stress dispersion and unique cell-by-cell and layer-by-layer fracture mechanisms among GSLSs, which are attributable to their reinforced diagonal struts and heterogeneous unit cells. Hence, three strategies for unit cell design and optimization were proposed. These discoveries guide the future design and optimization of lattice structures (LSs) and offer potential applications for GSLSs in aerospace, biomedicine, automobiles, architecture, and wearable products.

Spatial interaction and functional status of CD68+SHP2+ macrophages in tumor microenvironment correlate with overall survival of NSCLC

BackgroundTumor-associated macrophages (TAMs) constitute a plastic and heterogeneous cell population of the tumor microenvironment (TME) that can regulate tumor proliferation and support resistance to therapy, constituting promising targets for the development of novel anticancer agents. Our previous results suggest that SHP2 plays a crucial role in reprogramming the phenotype of TAMs. Thus, we hypothesized that SHP2+ TAM may predict the treatment efficacy of non-small cell lung cancer NSCLC patients as a biomarker.MethodsWe analyzed cancer tissue samples from 79 NSCLC patients using multiplex fluorescence (mIF) staining to visualize various SHP-2+ TAM subpopulations (CD68+SHP2+, CD68+CD86+, CD68 + 206+, CD68+ CD86+SHP2+, CD68+ CD206+SHP2+) and T cells (CD8+ Granzyme B +) of immune cells. The immune cells proportions were quantified in the tumor regions (Tumor) and stromal regions (Stroma), as well as in the overall tumor microenvironment (Tumor and Stroma, TME). The analysis endpoint was overall survival (OS), correlating them with levels of cell infiltration or effective density. Cox regression was used to evaluate the associations between immune cell subsets infiltration and OS. Correlations between different immune cell subsets were examined by Spearman’s tests.ResultsIn NSCLC, the distribution of different macrophage subsets within the TME, tumor regions, and stroma regions exhibited inconsistency. The proportions of CD68+ SHP2+ TAMs (P < 0.05) were higher in tumor than in stroma. And the high infiltration of CD68+SHP2+ TAMs in tumor areas correlated with poor OS (P < 0.05). We found that the expression level of SHP2 was higher in M2-like macrophages than in M1-like macrophages. The CD68+SHP2+ subset proportion was positively correlated with the CD68+CD206+ subset within TME (P < 0.0001), tumor (P < 0.0001) and stroma (P < 0.0001).ConclusionsThe high infiltration of CD68+SHP2+ TAMs predict poor OS in NSCLC. Targeting SHP2 is a potentially effective strategy to inhibit M2-phenotype polarization. And it provides a new thought for SHP2 targeted cancer immunotherapy.

Non-Tumor Cells within the Tumor Microenvironment—The “Eminence Grise” of the Glioblastoma Pathogenesis and Potential Targets for Therapy

Glioblastoma (GBM) is the most common malignancy of the central nervous system in adults. GBM has high levels of therapy failure and its prognosis is usually dismal. The phenotypic heterogeneity of the tumor cells, dynamic complexity of non-tumor cell populations within the GBM tumor microenvironment (TME), and their bi-directional cross-talk contribute to the challenges of current therapeutic approaches. Herein, we discuss the etiology of GBM, and describe several major types of non-tumor cells within its TME, their impact on GBM pathogenesis, and molecular mechanisms of such an impact. We also discuss their value as potential therapeutic targets or prognostic biomarkers, with reference to the most recent works on this subject. We conclude that unless all “key player” populations of non-tumor cells within the TME are considered, no breakthrough in developing treatment for GBM can be achieved.

CD4+ T cell heterogeneity in gestational age and preeclampsia using single-cell RNA sequencing

A balance between pro-inflammatory decidual CD4+ T cells and FOXP3+ regulatory T cells (FOXP3+ Tregs) is important for maintaining fetomaternal tolerance. Using single-cell RNA-sequencing and T cell receptor repertoire analysis, we determined that diversity and clonality of decidual CD4+ T cell subsets depend on gestational age. Th1/Th2 intermediate and Th1 subsets of CD4+ T cells were clonally expanded in both early and late gestation, whereas FOXP3+ Tregs were clonally expanded in late gestation. Th1/Th2 intermediate and FOXP3+ Treg subsets showed altered gene expression in preeclampsia (PE) compared to healthy late gestation. The Th1/Th2 intermediate subset exhibited elevated levels of cytotoxicity-related gene expression in PE. Moreover, increased Treg exhaustion was observed in the PE group, and FOXP3+ Treg subcluster analysis revealed that the effector Treg like subset drove the Treg exhaustion signatures in PE. The Th1/Th2 intermediate and effector Treg like subsets are possible inflammation-driving subsets in PE.

Pericytes Are Immunoregulatory Cells in Glioma Genesis and Progression

Vascular co-option is a consequence of the direct interaction between perivascular cells, known as pericytes (PCs), and glioblastoma multiforme (GBM) cells (GBMcs). This process is essential for inducing changes in the pericytes’ anti-tumoral and immunoreactive phenotypes. Starting from the initial stages of carcinogenesis in GBM, PCs conditioned by GBMcs undergo proliferation, acquire a pro-tumoral and immunosuppressive phenotype by expressing and secreting immunosuppressive molecules, and significantly hinder the activation of T cells, thereby facilitating tumor growth. Inhibiting the pericyte (PC) conditioning mechanisms in the GBM tumor microenvironment (TME) results in immunological activation and tumor disappearance. This underscores the pivotal role of PCs as a key cell in the TME, responsible for tumor-induced immunosuppression and enabling GBM cells to evade the immune system. Other cells within the TME, such as tumor-associated macrophages (TAMs) and microglia, have also been identified as contributors to this immunomodulation. In this paper, we will review the role of these three cell types in the immunosuppressive properties of the TME. Our conclusion is that the cellular heterogeneity of immunocompetent cells within the TME may lead to the misinterpretation of cellular lineage identification due to different reactive stages and the identification of PCs as TAMs. Consequently, novel therapies could be developed to disrupt GBM-PC interactions and/or PC conditioning through vascular co-option, thereby exposing GBMcs to the immune system.

Highlight of 2023: Advances in mast cells.

In this article for the Highlights of 2023 Series, we consider the growing understanding of mast cell heterogeneity and interactions that has developed from single cell RNA sequencing studies. We also discuss novel concepts concerning mast cell interactions with the central nervous system and evidence for their role in host defense against SARS-CoV-2 infection.

Cellular diversity through space and time: adding new dimensions to GBM therapeutic development

The current median survival for glioblastoma (GBM) patients is only about 16 months, with many patients succumbing to the disease in just a matter of months, making it the most common and aggressive primary brain cancer in adults. This poor outcome is, in part, due to the lack of new treatment options with only one FDA-approved treatment in the last decade. Advances in sequencing techniques and transcriptomic analyses have revealed a vast degree of heterogeneity in GBM, from inter-patient diversity to intra-tumoral cellular variability. These cutting-edge approaches are providing new molecular insights highlighting a critical role for the tumor microenvironment (TME) as a driver of cellular plasticity and phenotypic heterogeneity. With this expanded molecular toolbox, the influence of TME factors, including endogenous (e.g., oxygen and nutrient availability and interactions with non-malignant cells) and iatrogenically induced (e.g., post-therapeutic intervention) stimuli, on tumor cell states can be explored to a greater depth. There exists a critical need for interrogating the temporal and spatial aspects of patient tumors at a high, cell-level resolution to identify therapeutically targetable states, interactions and mechanisms. In this review, we discuss advancements in our understanding of spatiotemporal diversity in GBM with an emphasis on the influence of hypoxia and immune cell interactions on tumor cell heterogeneity. Additionally, we describe specific high-resolution spatially resolved methodologies and their potential to expand the impact of pre-clinical GBM studies. Finally, we highlight clinical attempts at targeting hypoxia- and immune-related mechanisms of malignancy and the potential therapeutic opportunities afforded by single-cell and spatial exploration of GBM patient specimens.

Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space.

Integrating multiple single-cell datasets is essential for the comprehensive understanding of cell heterogeneity. Batch effect is the undesired systematic variations among technologies or experimental laboratories that distort biological signals and hinder the integration of single-cell datasets. However, existing methods typically rely on a selected dataset as a reference, leading to inconsistent integration performance using different references, or embed cells into uninterpretable low-dimensional feature space. To overcome these limitations, a reference-free method, Beaconet, for integrating multiple single-cell transcriptomic datasets in original molecular space by aligning the global distribution of each batch using an adversarial correction network is presented. Through extensive comparisons with 13 state-of-the-art methods, it is demonstrated that Beaconet can effectively remove batch effect while preserving biological variations and is superior to existing unsupervised methods using all possible references in overall performance. Furthermore, Beaconet performs integration in the original molecular feature space, enabling the characterization of cell types and downstream differential expression analysis directly using integrated data with gene-expression features. Additionally, when applying to large-scale atlas data integration, Beaconet shows notable advantages in both time- and space-efficiencies. In summary, Beaconet serves as an effective and efficient batch effect removal tool that can facilitate the integration of single-cell datasets in a reference-free and molecular feature-preserved mode.

Quantitative Measurement of Plasma Membrane Protein Internalisation and Recycling in Heterogenous Cellular Samples by Flow Cytometry.

Plasma membrane proteins mediate important aspects of physiology, including nutrient acquisition, cell-cell interactions, and monitoring homeostasis. The trafficking of these proteins, involving internalisation from and/or recycling back to the cell surface, is often critical to their functions. These processes can vary among different proteins and cell types and states and are still being elucidated. Current strategies to measure surface protein internalisation and recycling are typically microscopy or biochemical assays; these are accurate but generally limited to analysing a homogenous cell population and are often low throughput. Here, we present flow cytometry-based methods involving probe-conjugated antibodies that enable quantification of internalisation or recycling rates at the single-cell level in complex samples. To measure internalisation, we detail an assay where the protein of interest is labelled with a specific antibody conjugated to a fluorescent oligonucleotide-labelled probe. To measure recycling, a specific antibody conjugated to a cleavable biotin group is employed. These probes permit the differentiation of molecules that have been internalised or recycled from those that have not. When combined with cell-specific marker panels, these methods allow the quantitative study of plasma membrane protein trafficking dynamics in a heterogenous cell mixture at the single-cell level. Key features • These assays allow sensitive quantification of internalised or recycled surface molecules using oligonucleotide or cleavable biotin-conjugated probes, respectively, and detected by flow cytometry. • They can be adapted to any membrane protein that transits via the cell surface and for which a specific purified antibody is available. • The dynamics of a cell surface protein can be measured in heterogenous cell populations simultaneously, including various cellular activation states. • The internalisation assay builds upon the method developed by Liu et al. [1,2] and extends its application to heterogenous human peripheral blood mononuclear cells. • These assays have been extensively used on suspension cells but have not been tested on adherent cells.

Longitudinal tracking of circulating rare events in the liquid biopsy of stage III–IV non-small cell lung cancer patients

In the United States, lung cancer is the second most common type of cancer with non-small cell lung cancer (NSCLC) encompassing around 85% of total lung cancer cases. Late-stage patients with metastatic disease have worsening prognosis, highlighting the importance of longitudinal disease monitoring. Liquid biopsy (LBx) represents a way for physicians to non-invasively track tumor analytes, such as circulating tumor cells (CTCs), and understand tumor progression in real-time through analyzing longitudinal blood samples. CTCs have been shown to be effective predictive biomarkers in measuring treatment efficacy and survival outcomes. We used the third-generation High-Definition Single Cell Assay (HDSCA3.0) workflow to analyze circulating rare events longitudinally during treatment in a cohort of 10 late-stage NSCLC patients, identifying rare events including circulating cancer cells (i.e., CTCs), and oncosomes. Here, we show (1) that there is a cancer specific LBx profile, (2) there is considerable heterogeneity of rare cells and oncosomes, and (3) that LBx data elements correlated with patient survival outcomes. Additional studies are warranted to understand the biological significance of the rare events detected, and the clinical potential of the LBx to monitor and predict response to treatment in NSCLC patient care.

Deciphering Müller cell heterogeneity signatures in diabetic retinopathy across species: an integrative single-cell analysis

Diabetic retinopathy (DR), a leading cause of visual impairment, demands a profound comprehension of its cellular mechanisms to formulate effective therapeutic strategies. Our study presentes a comprehensive single-cell analysis elucidating the intricate landscape of Müller cells within DR, emphasizing their nuanced involvement. Utilizing scRNA-seq data from both Sprague–Dawley rat models and human patients, we delineated distinct Müller cell clusters and their corresponding gene expression profiles. These findings were further validated through differential gene expression analysis utilizing human transcriptomic data. Notably, certain Müller cell clusters displayed upregulation of the Rho gene, implying a phagocytic response to damaged photoreceptors within the DR microenvironment. This phenomenon was consistently observed across species. Additionally, the co-expression patterns of RHO and PDE6G within Müller cell clusters provided compelling evidence supporting their potential role in maintaining retinal integrity during DR. Our results offer novel insights into the cellular dynamics of DR and underscore Müller cells as promising therapeutic targets for preserving vision in retinal disorders induced by diabetes.

Abstract PO1-15-01: Digital pathology models reveal case-specific characteristics of the tumor microenvironment

Abstract Background Phenotypic characteristics and genetic content have been observed to vary within individual tumors. However, translational research and some clinical practices often rely on extrapolating single histopathology slides to be representative of whole-tumor biology, even when the extent of intra-tumor biological heterogeneity is unknown. In addition, technical factors in slide preparation and digitization can also contribute to variability. Here, we use digital pathology models to identify cell and tissue types in whole-slide images of breast cancer tumors, and quantify intra- and inter-case heterogeneity in cell and tissue features of the tumor microenvironment. Methods We developed PathExplore, a suite of convolutional neural network models trained on pathologist annotations of cell and tissue types in hematoxylin and eosin-stained slides. The models were deployed on the TCGA breast cancer dataset (n=1083 primary solid tumors). Quantitative human-interpretable features (HIFs) relating to cancer, stromal, and necrotic tissue areas, as well as the abundance and distribution of cancer cells, fibroblasts, and immune cells within these tissues, were extracted for each slide. Similarity between slides on sets of HIFs was assessed using Pearson correlation. Intra- and inter-case variability of individual HIFs was measured using the normalized percent difference (range divided by mean) for each slide pair. Case-specificity was quantified by the AUC of correlation or difference distributions for intra-case vs inter-case slide pairs. Only pairs of slides with matching metadata, including tissue source site, cancer stage, and scanner model, were considered for inter-case analysis. Results A total of 55 cases, comprising 114 slides, were identified as having more than one slide per case. Considering all 123 proportional HIFs, intra-case slide pairs showed high correlation compared to inter-case pairs (r=0.97 vs r=0.90, AUC=0.88), with 29% of slides coming from a multi-slide case most closely correlated with another slide from the same case out of the entire TCGA breast dataset. Subsets of these proportional HIFs relating to each identified cell type were also case-specific, with AUC’s ranging from 0.79 for macrophage HIFs to 0.87 for cancer cell HIFs. All proportion, density, and ratio HIFs (n=297) individually showed case specificity (AUC >0.5), with median intra-case differences ranging from 0.6% to 69%. Area proportion of necrosis was more variable than cancer or stroma tissue, while cancer cell count proportions were less variable than fibroblast or immune cell proportions. Cell count proportions were in general less variable and more specific than their corresponding density HIFs; for instance, the frequency of fibroblasts out of all cells in stroma had a median intra-case difference of 11% compared to 20% for the density of fibroblasts in stroma. Conclusions We quantified heterogeneity of key features of the tumor microenvironment both within and across tumors. These results reveal biological and technical variability that can inform selection and interpretation of biomarkers derived from single slides. The ability to uniquely identify slides from the same case additionally demonstrates the technical robustness of digital pathology models for yielding quantitative insights into tumor biology. Citation Format: Ylaine Gerardin, Christian Kirkup, Archit Khosla, Laura Chambre, Michael Drage, Amaro Taylor-Weiner. Digital pathology models reveal case-specific characteristics of the tumor microenvironment [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-15-01.