Single-cell Phenotyping Research Articles

A biology-driven deep generative model for cell-type annotation in cytometry.

Cytometry enables precise single-cell phenotyping within heterogeneous populations. These cell types are traditionally annotated via manual gating, but this method lacks reproducibility and sensitivity to batch effect. Also, the most recent cytometers-spectral flow or mass cytometers-create rich and high-dimensional data whose analysis via manual gating becomes challenging and time-consuming. To tackle these limitations, we introduce Scyan https://github.com/MICS-Lab/scyan, a Single-cell Cytometry Annotation Network that automatically annotates cell types using only prior expert knowledge about the cytometry panel. For this, it uses a normalizing flow-a type of deep generative model-that maps protein expressions into a biologically relevant latent space. We demonstrate that Scyan significantly outperforms the related state-of-the-art models on multiple public datasets while being faster and interpretable. In addition, Scyan overcomes several complementary tasks, such as batch-effect correction, debarcoding and population discovery. Overall, this model accelerates and eases cell population characterization, quantification and discovery in cytometry.

Impedance-Based Multimodal Electrical-Mechanical Intrinsic Flow Cytometry.

Reflecting various physiological states and phenotypes of single cells, intrinsic biophysical characteristics (e.g., mechanical and electrical properties) are reliable and important, label-free biomarkers for characterizing single cells. However, single-modal mechanical or electrical properties alone are not specific enough to characterize single cells accurately, and it has been long and challenging to couple the conventionally image-based mechanical characterization and impedance-based electrical characterization. In this work, the spatial-temporal characteristics of impedance sensing signal are leveraged, and an impedance-based multimodal electrical-mechanical flow cytometry framework for on-the-fly high-dimensional intrinsic measurement is proposed, that is, Young's modulus E, fluidity β, radius r, cytoplasm conductivity σi , and specific membrane capacitance Csm , of single cells. With multimodal high-dimensional characterization, the electrical-mechanical flow cytometry can better reveal the difference in cell types, demonstrated by the experimental results with three types of cancer cells (HepG2, MCF-7, and MDA-MB-468) with 93.4% classification accuracy and pharmacological perturbations of the cytoskeleton (fixed and Cytochalasin B treated cells) with 95.1% classification accuracy. It is envisioned that multimodal electrical-mechanical flow cytometry provides a new perspective for accurate label-free single-cell intrinsic characterization.

Single-cell deep phenotyping of cytokine release unmasks stimulation-specific biological signatures and distinct secretion dynamics

Cytokines are important mediators of the immune system, and their secretion level needs to be carefully regulated, as an unbalanced activity may lead to cytokine release syndromes. Dysregulation can be induced by various factors, including immunotherapies. Therefore, the need for risk assessment during drug development has led to the introduction of cytokine release assays (CRAs). However, the current CRAs offer little insight into the heterogeneous cellular dynamics. To overcome this limitation, we developed an advanced single-cell microfluidic-based cytokine secretion platform to quantify cytokine secretion on the single-cell level dynamically. Our approach identified different dynamics, quantities, and phenotypically distinct subpopulations for each measured cytokine upon stimulation. Most interestingly, early measurements after only 1h of stimulation revealed distinct stimulation-dependent secretion dynamics and cytokine signatures. With increased sensitivity and dynamic resolution, our platform provided insights into the secretion behavior of individual immune cells, adding crucial additional information about biological stimulation pathways to traditional CRAs.

Fiber density and matrix stiffness modulate distinct cell migration modes in a 3D stroma mimetic composite hydrogel.

The peritumoral stroma is a complex 3D tissue that provides cells with myriad biophysical and biochemical cues. Histologic observations suggest that during metastatic spread of carcinomas, these cues influence transformed epithelial cells, prompting a diversity of migration modes spanning single cell and multicellular phenotypes. Purported consequences of these variations in tumor escape strategies include differential metastatic capability and therapy resistance. Therefore, understanding how cues from the peritumoral stromal microenvironment regulate migration mode has both prognostic and therapeutic value. Here, we utilize a synthetic stromal mimetic in which matrix fiber density and bulk hydrogel mechanics can be orthogonally tuned to investigate the contribution of these two key matrix attributes on MCF10A migration mode phenotypes, epithelial-mesenchymal transition (EMT), and invasive potential. We develop an automated computational image analysis framework to extract migratory phenotypes from fluorescent images and determine 3D migration metrics relevant to metastatic spread. Using this analysis, we find that matrix fiber density and bulk hydrogel mechanics distinctly contribute to a variety of MCF10A migration modes including amoeboid, single mesenchymal, clusters, and strands. We identify combinations of physical and soluble cues that induce a variety of migration modes originating from the same MCF10A spheroid and use these settings to examine a functional consequence of migration mode -resistance to apoptosis. We find that cells migrating as strands are more resistant to staurosporine-induced apoptosis than either disconnected clusters or individual invading cells. Improved models of the peritumoral stromal microenvironment and understanding of the relationships between matrix attributes and cell migration mode can aid ongoing efforts to identify effective cancer therapeutics that address cell plasticity-based therapy resistances. STATEMENT OF SIGNIFICANCE: Stromal extracellular matrix structure dictates both cell homeostasis and activation towards migratory phenotypes. However decoupling the effects of myriad biophysical cues has been difficult to achieve. Here, we encapsulate electrospun fiber segments within an amorphous hydrogel to create a fiber-reinforced hydrogel composite in which fiber density and hydrogel stiffness can be orthogonally tuned. Quantification of 3D cell migration reveal these two parameters uniquely contribute to a diversity of migration phenotypes spanning amoeboid, single mesenchymal, multicellular cluster, and collective strand. By tuning biophysical and biochemical cues to elicit heterogeneous migration phenotypes, we find that collective strands best resist apoptosis. This work establishes a composite approach to modulate fibrous topography and bulk hydrogel mechanics and identified biomaterial parameters to direct distinct 3D cell migration phenotypes.

The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI.

Growing evidence supports the critical role of tumour microenvironment (TME) in tumour progression, metastases, and treatment response. However, the in-situ interplay among various TME components, particularly between immune and tumour cells, are largely unknown, hindering our understanding of how tumour progresses and responds to treatment. While mainstream single-cell omics techniques allow deep, single-cell phenotyping, they lack crucial spatial information for in-situ cell-cell interaction analysis. On the other hand, tissue-based approaches such as hematoxylin and eosin and chromogenic immunohistochemistry staining can preserve the spatial information of TME components but are limited by their low-content staining. High-content spatial profiling technologies, termed spatial omics, have greatly advanced in the past decades to overcome these limitations. These technologies continue to emerge to include more molecular features (RNAs and/or proteins) and to enhance spatial resolution, opening new opportunities for discovering novel biological knowledge, biomarkers, and therapeutic targets. These advancements also spur the need for novel computational methods to mine useful TME insights from the increasing data complexity confounded by high molecular features and spatial resolution. In this review, we present state-of-the-art spatial omics technologies, their applications, major strengths, and limitations as well as the role of artificial intelligence (AI) in TME studies.

Single-cell transcriptional phenotypes linked to anatomical localization of fructose transporters in rat proximal tubule segments

Kidneys of healthy individuals filter 4 to 25 g of fructose (FRU) a day, equivalent to ~10% of the filtered glucose. FRU reabsorbed by proximal tubules (PTs) is mostly used in gluconeogenesis. Elevated dietary FRU alters hormonal signaling in PTs and increases oxidative stress, which overtime, leads to salt-sensitive hypertension, tubulointerstitial injury, and glomerular damage. Four apical transporters could transport FRU at physiological concentrations: SGLT5 (km0.62 mM), NAGLT1 (km4.5 mM), GLUT5 (km12.6 mM) and SGLT4 (undetermined km). We hypothesize that single-cell (sc) transcriptional phenotypes correspond to anatomical features and could inform FRU transport in PTs. We used scRNAseq rat kidney transcriptomes (3 males & 3 females) to measure the expression of FRU transporters in cell clusters corresponding to the S1, S2, and S3 PT segments. We integrated cell transcriptional phenotypes with spatial and structural features using quantitative proteomics and transcriptomics from microdissected PT segments. Clusters S1, S2, and S3 had 1281, 2123 and 1441 cells, respectively. More than 90% of cells in all clusters expressed KHK, the first enzyme in FRU metabolism (expression: S1<S2<S3), showing high correlation with segment-proteomics (PCC (Pearson correlation coefficient) = 0.87) and transcriptomics (PCC = 0.65). In contrast, there were striking imbalances in transporters’ distributions. High-affinity SGLT5 was expressed by 3%, 24%, 75% cells in clusters S1, S2, and S3, with 4, 43, 230 aggregated counts per 100 cells (nCounts), respectively. SGLT5 showed a log2FC > 0.8 in S3 as compared to S1-S2 (p-adj < 1x10-300). SGLT5, PCC was 0.95 for quantitative proteomics and 0.48 for sequencing. Low-affinity NAGLT1 was expressed by 70%, 81%, 47% cells in clusters S1, S2, and S3, with 206, 306, 121 nCounts, respectively. These values were highly correlated with RNAseq in microdissected tubules (PCC = 0.99), but NAGLT1 was absent in the proteomics dataset. GLUT5 was expressed by 9%, 10%, 38% cells in clusters S1, S2 and S3, with 11, 11, 63 nCounts, respectively, and PCC > 0.95 for both quantitative proteomics and sequencing. Finally, SGLT4 was expressed by 4%, 6%, 8% cell in clusters S1, S2 and S3, with 5, 6, 8 nCounts, respectively. Due to the low signal in microdissected segments, we could not conduct a correlation analysis for SGLT4. Our data shows that SGLT5 is most abundant in the straight portion of PTs, particularly S3. GLUT5 and SGLT4 follow a similar distribution. In contrast, low-affinity NAGLT1 is expressed in all segments, most predominantly S1. However, as NAGLT1 affinity for glucose (km3.7 mM) is near that for FRU, it likely reabsorbs glucose in early PT. In the straight portion FRU will be predominantly reabsorbed by the most abundant higher-affinity SGLT5. Our analysis shows a high correlation between scRNAseq clusters and spatial proteomics data and highlights the relevance of SGLT5 as the main FRU transporter in PTs BGT670107 This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Design and Validation of Ultrahigh-plex Discovery Panels for Immune Checkpoint Targets

Abstract Ultrahigh-plex single-cell spatial phenotyping is revolutionizing immuno-oncology research. To enable in-depth characterization of the tumor microenvironment (TME), we have developed and validated pre-optimized antibody panels on cancer tissues to reveal key cell types and cellular architecture using the PhenoCycler™-Fusion system. We designed multiple panels that answer specific biological questions about the TME, and once multiplexed together create a comprehensive view of the structure and function of the TME. The resulting PhenoCode™ Discovery panels include markers that identify immune cell types, activation states, immune checkpoints, and mediators of proliferation. Each antibody in the panel was conjugated to an oligo barcode and stained on human FFPE tissue. Staining specificity of each conjugated antibody was assessed by comparing immunofluorescent results from PhenoCycler™ with a DAB chromogenic immunohistochemistry assay. The slide stained with a PhenoCode™ Discovery panel was imaged on a PhenoCycler™-Fusion platform, where dye-labeled oligo reporters (complementary to the barcodes) are hybridized to the barcode to visualize the antibody. Antibody concentration, exposure time, and corresponding dye were further optimized, verified, and validated as a whole module using tonsil and cancer tissues based on image analysis and intensity analysis. In this study, we showcased the rigorous process used for development and validation of PhenoCode™ Discovery panels and demonstrated how they can work together to collect comprehensive data from one tissue sample. The introduction of these pre-optimized antibody panels marks a pivotal point in the evolution of ultrahigh-plex spatial phenotyping.

Single-cell Spatial Phenotyping of Immune and Metabolic Landscapes in Systemic Lupus Erythematosus

Abstract Autoimmune diseases like systemic lupus erythematosus (SLE) affect the health of multiple organ systems including the central nervous system, skin, joints, and kidneys. The heterogeneity of clinical profiles observed with SLE reflects a complex dysregulation of cellular processes, including metabolism, apoptotic clearance, and inflammation. Diagnosis and treatment of SLE is thus challenging, and few new treatments have been approved over the past 60 years. In this study, we aimed to characterize the spatial immune and metabolic profiles of SLE at single cell resolution. To do so, we profiled a cohort of human FFPE SLE tissues with an ultrahigh-plex panel of >50 oligo-conjugated antibodies representing immune, apoptotic, metabolic and stress markers. Whole-slide, spatial phenotyping was performed using the PhenoCycler ®-Fusion system, followed by deep bioinformatic analyses to determine the cellular phenotypes, spatial neighborhoods, and metabolic determinants of SLE. By combining immune cell lineage information with expression of key proteins involved in glycolysis, amino acid metabolism, lipid metabolism, hypoxia, oxidative stress, and apoptosis, we elucidated the functional dysregulation in key immune subsystems that may contribute to development of autoimmunity and inflammation associated with SLE. Spatial phenotyping also revealed a high degree of inter- and intra-sample heterogeneity, confirming the complexity of the SLE clinical profile. Collectively, our data amount to new insights into metabolic reprogramming of immune cells in autoimmune diseases and may aid in the identification of novel therapeutic biomarkers within the tissue microenvironment of SLE patients.

Accurate and Rapid Detection of Peritoneal Metastasis from Gastric Cancer by AI-Assisted Stimulated Raman Molecular Cytology.

Peritoneal metastasis (PM) is the mostcommon form of distant metastasis and one of the leading causes of death in gastriccancer (GC). For locally advanced GC, clinical guidelines recommend peritoneal lavage cytology for intraoperative PM detection. Unfortunately, current peritoneal lavage cytology is limited by low sensitivity (<60%). Here the authors established the stimulated Raman molecular cytology (SRMC), a chemical microscopy-based intelligent cytology. The authors firstly imaged 53 951 exfoliated cells in ascites obtained from 80 GC patients (27 PM positive, 53 PM negative). Then, the authors revealed 12 single cell features of morphology and composition that are significantly different between PM positive and negative specimens, including cellular area, lipid protein ratio, etc. Importantly, the authors developed a single cell phenotyping algorithm to further transform the above raw features to feature matrix. Such matrix is crucial to identify the significant marker cell cluster, the divergence of which is finally used to differentiate the PM positive and negative. Compared with histopathology, the gold standard of PM detection, their SRMC method could reach 81.5% sensitivity, 84.9% specificity, and the AUC of 0.85, within 20 minutes for each patient. Together, their SRMC method shows great potential for accurate and rapid detection of PM from GC.

Ex vivo drug response heterogeneity reveals personalized therapeutic strategies for patients with multiple myeloma

Multiple myeloma (MM) is a plasma cell malignancy defined by complex genetics and extensive patient heterogeneity. Despite a growing arsenal of approved therapies, MM remains incurable and in need of guidelines to identify effective personalized treatments. Here, we survey the ex vivo drug and immunotherapy sensitivities across 101 bone marrow samples from 70 patients with MM using multiplexed immunofluorescence, automated microscopy and deep-learning-based single-cell phenotyping. Combined with sample-matched genetics, proteotyping and cytokine profiling, we map the molecular regulatory network of drug sensitivity, implicating the DNA repair pathway and EYA3 expression in proteasome inhibitor sensitivity and major histocompatibility complex class II expression in the response to elotuzumab. Globally, ex vivo drug sensitivity associated with bone marrow microenvironmental signatures reflecting treatment stage, clonality and inflammation. Furthermore, ex vivo drug sensitivity significantly stratified clinical treatment responses, including to immunotherapy. Taken together, our study provides molecular and actionable insights into diverse treatment strategies for patients with MM.

Rapid single-cell physical phenotyping of mechanically dissociated tissue biopsies

During surgery, rapid and accurate histopathological diagnosis is essential for clinical decision making. Yet the prevalent method of intra-operative consultation pathology is intensive in time, labour and costs, and requires the expertise of trained pathologists. Here we show that biopsy samples can be analysed within 30 min by sequentially assessing the physical phenotypes of singularized suspended cells dissociated from the tissues. The diagnostic method combines the enzyme-free mechanical dissociation of tissues, real-time deformability cytometry at rates of 100–1,000 cells s−1 and data analysis by unsupervised dimensionality reduction and logistic regression. Physical phenotype parameters extracted from brightfield images of single cells distinguished cell subpopulations in various tissues, enhancing or even substituting measurements of molecular markers. We used the method to quantify the degree of colon inflammation and to accurately discriminate healthy and tumorous tissue in biopsy samples of mouse and human colons. This fast and label-free approach may aid the intra-operative detection of pathological changes in solid biopsies.

Abstract 6768: The potential predictive role of spatial phenotyping in non-small cell lung cancer

Abstract Background Lung cancers are the leading cause of cancer-related deaths with a 5-year-survival of only ~20%. Whilst immunotherapies have led to durable and prolonged survival, only a subset of patients remain responsive. Additional biomarkers are thus needed to better predict if patients will respond or develop resistance against immune checkpoint inhibitor (ICI) therapies. Spatial phenotyping of the tumor microenvironment (TME) is now recognized as a proxy for ICI therapy outcomes. Our study employs an end-to-end spatial biology strategy to survey non-small-cell lung cancer (NSCLC) tissues for new biomarkers that could guide immunotherapy treatments. Methods We phenotyped pre-treatment biopsies from non-small-cell lung cancer (NSCLC) patients treated with single-agent Nivolumab. We first performed 57-plex whole-slide Single Cell Spatial Phenotyping on the PhenoCycler®-Fusion platform. Next, we developed the spatial analysis of a larger NSCLC cohort using customizable PhenoCode Signature Panels (PSP) for high-throughput immune profile (CD3/CD8/CD20/CD68/PanCK + CD4 add-in) and immuno-contexture (CD8/CD68/PD-L1/FoxP3/PanCK + PD-1 add-in) imaging. The PSP panel content combines the barcode-based antibody chemistry from the PhenoCycler platform with the signal amplification of Opal chemistry from the PhenoImager platform. PSP panels were profiled across n=27 NSCLC biopsies and provided a wider breadth of tissue profiling. Results Our whole-slide single-cell spatial phenotyping analyses revealed high phenotypic diversity in the TME of patients responsive and resistant to ICI therapy. However, higher-throughput analyses of the same tissues using targeted PSP panels revealed no significant differences in quantities of immune cell lineages, including T-cells and macrophages. On the contrary, we discovered multiple quantifiable and statistically significant spatial signatures that appear to be predictive of treatment benefit, which confirms the potential biomarker value of spatial associations in patient tissues. Conclusions This study amounts to a uniquely comprehensive Single Cell Spatial Phenotyping analysis of pre-treatment NSCLC biopsies from a single-agent Nivolumab study. Our data catalogue the diversity in the immune microenvironment of NSCLC but highlighted that immune cell quantification is insufficient to stratify patient cohorts. Single-cell spatial phenotyping, on the other hand, promises to reveal new biomarkers that may aid in better stratification of patients in pre-treatment evaluations. Citation Format: Ning Ma, Aditya Pratapa, James Monkman, Ken O’Byrne, Oliver Braubach, Arutha Kulasinghe. The potential predictive role of spatial phenotyping in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6768.

Abstract 2059: Machine learning integration of transcriptome-wide spatial sequencing data and ultra-high plex spatial proteomic data enables the prioritization of cancer drug targets

Abstract Spatial omics technologies are producing an unprecedented amount of ultra-high plex in situ data that are promising to revolutionize cancer prognoses and treatments. Analytical solutions to integrate big spatial data are, however, lagging relative to the rapid technology development and this hinders discoveries into the pathological processes underlying cancer initiation and progression. Here we present STimage, a machine learning approach to flexibly combine transcriptome-wide spatial sequencing data with single-cell protein-based spatial phenotyping (Phenocycler-Fusion), generated from same tissue samples. As a ground-truth for cell typing, Akoya’s single-cell spatial phenotyping technology enabled us to precisely define cell types and cell states that were then used to evaluate and deploy the data integration pipeline. With these data, STimage first maps cells onto tissue sections with reference an H&amp;E image. This way multiple layers of molecular data are transferred into one common framework, which can then be analyzed together. Traditional pathology annotations on the H&amp;E images are also integrated to add human understanding of morphological patterns in a cancer tissue. The integrated analysis improves cell neighborhood identification, which allows cell-cell interaction analysis based on spatial co-localization between cell types (using single-cell resolution protein data) and locally co-expressing ligand-receptor pairs (using transcriptome-wide spatial data). We applied STimage to head and neck and skin cancer samples, in order to demonstrate the broad applicability of this analysis pipeline for various cancer types. We demonstrate applications for both, diagnoses and prognoses. For diagnosis, ST image identified and cross-validated cell types whilst assessing the expression of markers for drug targets. We also present an in-depth case study for an oropharyngeal squamous cell carcinoma patient not responding to Nivolumab treatment (anti-PD-1). Based on Visium and PhenoCycler-Fusion data, we discovered a spatial signature for non-responsiveness. Furthermore, we used the predicted ligand-receptor interactions to rank the patient’s response potential to currently available drugs, with the top target being TF-TFRC. STimage is thus able to integrate multiple layers of spatial omics data to improve and solidify prognostic biomarkers for cancer treatments. This study highlights the power of machine learning integration to combine multiple spatial multi-omics data, in particular PhenoCycler-Fusion and Visium, for improving diagnosis, prognosis and treatment of diverse cancers. Citation Format: Xiao Tan, Andrew Causer, Jazmina Gonzales-Cruz, Ning Ma, Bassem Ben Cheikh, Oliver Braubach, Quan Nguyen. Machine learning integration of transcriptome-wide spatial sequencing data and ultra-high plex spatial proteomic data enables the prioritization of cancer drug targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2059.

Abstract 4649: Design and validation of ultrahigh-plex discovery panels for immuno-oncology and oncology

Abstract Introduction Ultrahigh-plex single-cell spatial phenotyping is revolutionizing cancer research through deeper interrogation of cellular and protein-level co-expression, localization, and arrangements within cancer tissues. To enable in-depth characterization of the tumor microenvironment (TME), we have developed and validated pre-optimized antibody panels on cancer tissues to reveal key cell types and cellular architecture using the PhenoCycler™-Fusion system. Here, we present our methodology for the development and validation of multiplex spatial phenotyping panels for oncology and immuno-oncology. Methods We designed panel modules such that each module answers specific biological questions about the TME and can be multiplexed together for a more comprehensive view of the structure and function of the tumor and TME. The resulting PhenoCode™ Discovery panel modules include markers that identify key immune cell types, immune activations and checkpoints, mediators of proliferation, and other hallmarks of cancer. Each antibody in the panel module was conjugated to an oligo barcode and stained on human formalin-fixed paraffin-embedded (FFPE) tissue. The slide was imaged on a PhenoCycler™-Fusion platform, where dye-labeled oligo reporters (complementary to the barcodes) are hybridized to the barcode to visualize the antibody. The results were compared serial sections run with a 3,3’-Diaminobenzidine (DAB) chromogenic immunohistochemistry assay to ensure specificity of the antibody was retained. Antibody concentration, exposure time, and corresponding dye were further optimized, verified, and validated as a whole module using tonsil and cancer tissues based on image analysis and intensity analysis, to achieve an ultrahigh-plex detection. Results and Conclusions In this study, we showcased the rigorous process used for development and validation of PhenoCode™ Discovery panel modules and demonstrated how they can seamlessly work together to collect more data out of one tissue sample. The introduction of these pre-optimized antibody panel modules mark a pivotal point in the evolution of ultrahigh-plex spatial phenotyping. They not only simplify experimental design and assay set up, with panels that make it easier for researchers to answer important questions with this technology, but also save precious time and resources, while reducing overall costs. Citation Format: Yan He, Shannon Berry, Olive Shang, Michael McLane, Nicholas Ihley, Yi Zheng. Design and validation of ultrahigh-plex discovery panels for immuno-oncology and oncology. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4649.

Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers

Although breast cancer mortality is largely caused by metastasis, clinical decisions are based on analysis of the primary tumor and on lymph node involvement but not on the phenotype of disseminated cells. Here, we use multiplex imaging mass cytometry to compare single-cell phenotypes of primary breast tumors and matched lymph node metastases in 205 patients. We observe extensive phenotypic variability between primary and metastatic sites and that disseminated cell phenotypes frequently deviate from the clinical disease subtype. We identify single-cell phenotypes and spatial organizations of disseminated tumor cells that are associated with patient survival and a weaker survival association for high-risk phenotypes in the primary tumor. We show that p53 and GATA3 in lymph node metastases provide prognostic information beyond clinical classifiers and can be measured with standard methods. Molecular characterization of disseminated tumor cells is an untapped source of clinically applicable prognostic information for breast cancer.

Diversity and Clonality of T Cell Receptor Repertoire and Antigen Specificities in Small Joints of Early Rheumatoid Arthritis.

CD4+ T cells are implicated in rheumatoid arthritis (RA) pathology from the strong association between RA and certain HLA class II gene variants. This study was undertaken to examine the synovial T cell receptor (TCR) repertoire, T cell phenotypes, and T cell specificities in small joints of RA patients at time of diagnosis before therapeutic intervention. Sixteen patients, of whom 11 patients were anti-citrullinated protein antibody (ACPA)-positive and 5 patients were ACPA-, underwent ultrasound-guided synovial biopsy of a small joint (n=13) or arthroscopic synovial biopsy of a large joint (n=3), followed by direct sorting of single T cells for paired sequencing of the αβ TCR together with flow cytometry analysis. TCRs from expanded CD4+ T cell clones of 4 patients carrying an HLA-DRB1*04:01 allele were artificially reexpressed to study antigen specificity. T cell analysis demonstrated CD4+ dominance and the presence of peripheral helper T-like cells in both patient groups. We identified >4,000 unique TCR sequences, as well as 225 clonal expansions. Additionally, T cells with double α-chains were a recurring feature. We identified a biased gene usage of the Vβ chain segment TRBV20-1 in CD4+ cells from ACPA+ patients. In vitro stimulation of T cell lines expressing selected TCRs with an extensive panel of citrullinated and viral peptides identified several different virus-specific TCRs (e.g., human cytomegalovirus and human herpesvirus 2). Still, the majority of clones remained orphans with unknown specificity. Minimally invasive biopsies of the RA synovium allow for single-cell TCR sequencing and phenotyping. Clonally expanded, viral-reactive T cells account for part of the diverse CD4+ T cell repertoire. TRBV20-1 bias in ACPA+ patients suggests recognition of common antigens.

PD-1+CD8+ T Cells Proximal to PD-L1+CD68+ Macrophages Are Associated with Poor Prognosis in Pancreatic Ductal Adenocarcinoma Patients

Immune complexity status in the TME has been linked to clinical outcomes in pancreatic ductal adenocarcinoma (PDAC) patients. TME assessments with current cell marker and cell density-based analyses do not identify the original phenotypes of single cells with multilineage selectivity, the functional status of the cells, or cellular spatial information in the tissues. Here, we describe a method that circumvents these problems. The combined strategy of multiplexed IHC with computational image cytometry and multiparameter cytometric quantification allows us to assess multiple lineage-selective and functional phenotypic biomarkers in the TME. Our study revealed that the percentage of CD8+ T lymphoid cells expressing the T cell exhaustion marker PD-1 and the high expression of the checkpoint PD-L1 in CD68+ cells are associated with a poor prognosis. The prognostic value of this combined approach is more significant than that of lymphoid and myeloid cell density analyses. In addition, a spatial analysis revealed a correlation between the abundance of PD-L1+CD68+ tumor-associated macrophages and PD-1+CD8+T cell infiltration, indicating pro-tumor immunity associated with a poor prognosis. These data highlight the implications of practical monitoring for understanding the complexity of immune cells in situ. Digital imaging and multiparameter cytometric processing of cell phenotypes in the TME and tissue architecture can reveal biomarkers and assessment parameters for patient stratification.

Investigation of breast cancer metabolic plasticity in the tumor microenvironment by single-cell organelle phenotyping.

Metabolic plasticity, i.e., the capability of cells to modify their metabolic state, is a hallmark of cancer and an important factor in the formation of metastases as well as a major contributor to chemoresistance and tumor recurrence. Furthermore, the tumor microenvironment, namely the collection of environmental properties (pH, nutrient availability, fatty acids) and cell types that surround the main tumor mass, is regarded to as the main driver of the metabolic state of tumor cells. Unfortunately, no technique can non-invasively assess the metabolic state of single cells in living samples, limiting our understanding of the heterogeneity and dynamics of metabolic state transitions. Here, we apply a technique, named ESPRESSO (Environmental Sensors Profiling Relayed by Subcellular Structures and Organelles) that combines organelle-specific environment-sensitive probes (ESPs) with hyperspectral imaging and quantitative bioimage analysis. We use a mixture of lipid droplet-, mitochondria- and lysosome-specific ESPs to quantify of morphological (e.g., number, size, organization) and functional (e.g., membrane potential, pH, polarity) characteristics to identify the metabolic state in single, living cells. To understand the differences in metabolic plasticity of non-tumorigenic breast epithelial (MCF10a) and triple negative breast cancer (MDA-MB-231) cells, we determined their metabolic state with ESPRESSO under a variety of stress and environmental conditions, identifying the characteristic metabolic signature of the cell lines under those conditions. We further investigated the response to diverse chemotherapy drugs (doxorubicin, paclitaxel, curcumin) in an effort to understand their effect on cancer and healthy cells, highlighting the environmental conditions that would favor the resistance to a specific drug treatment. Taken together, we provide a framework to identify the metabolic response of cancer and non-tumorigenic cells under different drugs, stressors and environmental conditions, providing in-depth characterization of their metabolic response at the single cell level.

Single-cell spatial landscapes of the lung tumour immune microenvironment

Single-cell technologies have revealed the complexity of the tumour immune microenvironment with unparalleled resolution1–9. Most clinical strategies rely on histopathological stratification of tumour subtypes, yet the spatial context of single-cell phenotypes within these stratified subgroups is poorly understood. Here we apply imaging mass cytometry to characterize the tumour and immunological landscape of samples from 416 patients with lung adenocarcinoma across five histological patterns. We resolve more than 1.6 million cells, enabling spatial analysis of immune lineages and activation states with distinct clinical correlates, including survival. Using deep learning, we can predict with high accuracy those patients who will progress after surgery using a single 1-mm2 tumour core, which could be informative for clinical management following surgical resection. Our dataset represents a valuable resource for the non-small cell lung cancer research community and exemplifies the utility of spatial resolution within single-cell analyses. This study also highlights how artificial intelligence can improve our understanding of microenvironmental features that underlie cancer progression and may influence future clinical practice.

Single-cell RNA-based phenotyping reveals a pivotal role of thyroid hormone receptor alpha for hypothalamic development.

Thyroid hormone and its receptor TRα1 play an important role in brain development. Several animal models have been used to investigate this function, including mice heterozygous for the TRα1R384C mutation, which confers receptor-mediated hypothyroidism. These mice display abnormalities in several autonomic functions, which was partially attributed to a developmental defect in hypothalamic parvalbumin neurons. However, whether other cell types in the hypothalamus are similarly affected remains unknown. Here, we used single-nucleus RNA sequencing to obtain an unbiased view on the importance of TRα1 for hypothalamic development and cellular diversity. Our data show that defective TRα1 signaling has surprisingly little effect on the development of hypothalamic neuronal populations, but it heavily affects hypothalamic oligodendrocytes. Using selective reactivation of the mutant TRα1 during specific developmental periods, we find that early postnatal thyroid hormone action seems to be crucial for proper hypothalamic oligodendrocyte maturation. Taken together, our findings underline the well-known importance of postnatal thyroid health for brain development and provide an unbiased roadmap for the identification of cellular targets of TRα1 action in mouse hypothalamic development.