Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis

Background: High throughput single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for exploring cellular heterogeneity among complex human cancers. scRNA-Seq studies using fresh human surgical tissue are logistically difficult, preclude histopathological triage of samples, and limit the ability to perform batch processing. This hindrance can often introduce technical biases when integrating patient datasets and increase experimental costs. Although tissue preservation methods have been previously explored to address such issues, it is yet to be examined on complex human tissues, such as solid cancers and on high throughput scRNA-Seq platforms. Methods: Using the Chromium 10X platform, we sequenced a total of ~ 120,000 cells from fresh and cryopreserved replicates across three primary breast cancers, two primary prostate cancers and a cutaneous melanoma. We performed detailed analyses between cells from each condition to assess the effects of cryopreservation on cellular heterogeneity, cell quality, clustering and the identification of gene ontologies. In addition, we performed single-cell immunophenotyping using CITE-Seq on a single breast cancer sample cryopreserved as solid tissue fragments. Results: Tumour heterogeneity identified from fresh tissues was largely conserved in cryopreserved replicates. We show that sequencing of single cells prepared from cryopreserved tissue fragments or from cryopreserved cell suspensions is comparable to sequenced cells prepared from fresh tissue, with cryopreserved cell suspensions displaying higher correlations with fresh tissue in gene expression. We showed that cryopreservation had minimal impacts on the results of downstream analyses such as biological pathway enrichment. For some tumours, cryopreservation modestly increased cell stress signatures compared to freshly analysed tissue. Further, we demonstrate the advantage of cryopreserving whole-cells for detecting cell-surface proteins using CITE-Seq, which is impossible using other preservation methods such as single nuclei-sequencing. Conclusions: We show that the viable cryopreservation of human cancers provides high-quality single-cells for multiomics analysis. Our study guides new experimental designs for tissue biobanking for future clinical single-cell RNA sequencing studies

STAT3 gain-of-function mutations connect leukemia with autoimmune disease by pathological NKG2Dhi CD8+T cell dysregulation and accumulation

The association between cancer and autoimmune disease is unexplained, exemplified by T cell large granular lymphocytic leukemia (T-LGL) where gain-of-function (GOF) somatic STAT3 mutations correlate with co -exist-ing autoimmunity. To investigate whether these mutations are the cause or consequence of CD8+ T cell clonal expansions and autoimmunity, we analyzed patients and mice with germline STAT3 GOF mutations. STAT3 GOF mutations drove the accumulation of effector CD8+ T cell clones highly expressing NKG2D, the receptor for stress-induced MHC-class-I-related molecules. This subset also expressed genes for granzymes, perforin, interferon-y, and Ccl5/Rantes and required NKG2D and the IL-15/IL-2 receptor IL2RB for maximal accumula-tion. Leukocyte-restricted STAT3 GOF was sufficient and CD8+ T cells were essential for lethal pathology in mice. These results demonstrate that STAT3 GOF mutations cause effector CD8+ T cell oligoclonal accumu-lation and that these rogue cells contribute to autoimmune pathology, supporting the hypothesis that somatic mutations in leukemia/lymphoma driver genes contribute to autoimmune disease.Peer reviewe

Development and implementation of multimodal single cell technologies for human immune profiling

High-throughput single-cell RNA Sequencing (scRNA-seq) is a powerful platform for the characterisation of immune cells found in complex systems such as the tumour microenvironment. Yet many technical limitations remain with scRNA-seq, which if solved, could significantly improve the experimental readout and consequently the biologic insights obtained. Notably, these scRNA-seq methods only provide short-read sequences from one end of a cDNA template, making them poorly suited to the investigation of gene-regulatory events such as alternative RNA splicing, immunoglobulin gene rearrangements, or somatic genome evolution. Moreover, the intrinsic constraints set by relying solely on the measurement of RNA expression for the characterisation of cells remain problematic. RNA expression correlates poorly with protein expression yet the functionalities of a cellular unit within its system is enacted by the proteome, the final product of RNA. Consequently, scRNA-seq data provides an incomplete and possibly short-lived snapshot of cell function. To address the first challenge, I co-developed a method that combines targeted long read sequencing with short-read-based transcriptome profiling of barcoded single-cell libraries generated by droplet-based partitioning. We coined this method Repertoire And Gene Expression by Sequencing (RAGE-seq). We demonstrate the utility of this method by showing how somatic hypermutation, alternate splicing and clonal evolution of lymphocytes can be tracked across tissue compartments, defining signatures of expanded clones. To mitigate some of the disadvantages of relying solely on RNA expression to describe a cell, we implement the use of antibodies conjugated to poly-adenylated DNA barcodes. This allows the measurement of expression for hundreds of proteins, simultaneously with the transcriptome, for each individual cell. We illustrate how using the combined modalities of RNA & protein measurements allows for significantly improved characterisation of immune cells across blood, tonsil, melanoma, and breast cancer. Lastly, using multimodal single-cell technologies, we provide a comprehensive cellular immune atlas of breast cancer, where we identify multiple novel immune cell subsets found to significantly correlate with the prognosis of breast cancer patients. In summary, this thesis outlines the development, implementation, and utility of multimodal single-cell technologies for improved in-depth human immune profiling of tumours

Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions

In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases

Spatial Deconvolution of HER2-positive Breast Tumors Reveals Novel Intercellular Relationships

In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra-and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. We integrate and spatially map tumor-associated types from single cell data to find: segregated epithelial cells, interactions between B and T-cells and myeloid cells, co-localization of macrophage and T-cell subsets. A model is constructed to infer presence of tertiary lymphoid structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define novel interactions between tumor-infiltrating cells in breast cancer and provide tools generalizing across tissues and diseases

Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures

Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumour purities and cell lineages, to compare the performance of nine TME deconvolution methods (BayesPrism, Scaden, CIBERSORTx, MuSiC, DWLS, hspe, CPM, Bisque, and EPIC). Some methods are more robust in deconvolving mixtures with high tumour purity levels. Most methods tend to mis-predict normal epithelial for cancer epithelial as tumour purity increases, a finding that is validated in two independent datasets. The breast cancer molecular subtype influences this mis-prediction. BayesPrism and DWLS have the lowest combined numbers of false positives and false negatives, and have the best performance when deconvolving granular immune lineages. Our findings highlight the need for more single-cell characterisation of rarer cell types, and suggest that tumour cell compositions should be considered when deconvolving the TME.</p

Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures

Abstract Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumour purities and cell lineages, to compare the performance of nine TME deconvolution methods (BayesPrism, Scaden, CIBERSORTx, MuSiC, DWLS, hspe, CPM, Bisque, and EPIC). Some methods are more robust in deconvolving mixtures with high tumour purity levels. Most methods tend to mis-predict normal epithelial for cancer epithelial as tumour purity increases, a finding that is validated in two independent datasets. The breast cancer molecular subtype influences this mis-prediction. BayesPrism and DWLS have the lowest combined numbers of false positives and false negatives, and have the best performance when deconvolving granular immune lineages. Our findings highlight the need for more single-cell characterisation of rarer cell types, and suggest that tumour cell compositions should be considered when deconvolving the TME

Reversal of lactate and PD-1-mediated macrophage immunosuppression controls growth of PTEN/p53-deficient prostate cancer

PURPOSEPTEN loss-of-function occurs in ~50% of metastatic, castrate-resistant prostate cancer (mCRPC) patients, and associated with poor prognosis and responsiveness to standard-of-care therapies and immune checkpoint inhibitors. While PTEN loss-of-function hyperactivates PI3K signaling, combinatorial PI3K/AKT pathway and androgen deprivation therapy (ADT) has demonstrated limited anti-cancer efficacy in clinical trials. Here, we aimed to elucidate mechanism(s) of resistance to ADT/PI3K-AKT axis blockade, and to develop rational combinatorial strategies to effectively treat this molecular subset of mCRPC. EXPERIMENTAL DESIGNProstate-specific PTEN/p53-deficient genetically engineered mice (GEM) with established 150-200 mm3 tumors, as assessed by ultrasound, were treated with either ADT (degarelix), PI3K inhibitor (copanlisib), or anti-PD-1 antibody (aPD-1), as single agents or their combinations, and tumors were monitored by MRI and harvested for immune, transcriptomic and proteomic profiling, or ex vivo co-culture studies. Single-cell RNAseq on human mCRPC samples was performed using 10X Genomics platform. RESULTSCo-clinical trials in PTEN/p53-deficient GEM revealed that recruitment of PD-1-expressing tumor-associated macrophages (TAM) thwarts ADT/PI3Ki combination-induced tumor control. The addition of aPD-1 to ADT/PI3Ki combination led to TAM-dependent ~3-fold increase in anti-cancer responses. Mechanistically, decreased lactate production from PI3Ki-treated tumor cells suppressed histone lactylation within TAM, resulting in their anti-cancer phagocytic activation, which was augmented by ADT/aPD-1 treatment and abrogated by feedback activation of Wnt/β-catenin pathway. Single-cell RNA-sequencing analysis in mCRPC patient biopsy samples revealed a direct correlation between high glycolytic activity and TAM phagocytosis suppression. CONCLUSIONSImmunometabolic strategies that reverse lactate and PD-1-mediated TAM immunosuppression, in combination with ADT, warrant further investigation in PTEN-deficient mCRPC patients

Cross-tissue single-cell landscape of human monocytes and macrophages in health and disease

10.1016/j.immuni.2021.07.007IMMUNITY5481883-

A single-cell and spatially resolved atlas of human breast cancers

Breast cancers are complex cellular ecosystems where heterotypic interactions play central roles in disease progression and response to therapy. However, our knowledge of their cellular composition and organization is limited. Here we present a single-cell and spatially resolved transcriptomics analysis of human breast cancers. We developed a single-cell method of intrinsic subtype classification (SCSubtype) to reveal recurrent neoplastic cell heterogeneity. Immunophenotyping using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) provides high-resolution immune profiles, including new PD-L1/PD-L2+ macrophage populations associated with clinical outcome. Mesenchymal cells displayed diverse functions and cell-surface protein expression through differentiation within three major lineages. Stromal-immune niches were spatially organized in tumors, offering insights into antitumor immune regulation. Using single-cell signatures, we deconvoluted large breast cancer cohorts to stratify them into nine clusters, termed ‘ecotypes’, with unique cellular compositions and clinical outcomes. This study provides a comprehensive transcriptional atlas of the cellular architecture of breast cancer