Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but does not capture their spatial distribution nor reveal local networks of intercellular communication acting in ...situ. A suite of recently developed techniques that localize RNA within tissue, including multiplexed in situ hybridization and in situ sequencing (here defined as high-plex RNA imaging) and spatial barcoding, can help address this issue. However, no method currently provides as complete a scope of the transcriptome as does scRNA-seq, underscoring the need for approaches to integrate single-cell and spatial data. Here, we review efforts to integrate scRNA-seq with spatial transcriptomics, including emerging integrative computational methods, and propose ways to effectively combine current methodologies.
To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging ...from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer.
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•Profiling of 10 human skin SCCs and matched normals via scRNA-seq, ST, and MIBI•Tumor-specific keratinocytes (TSKs) reside within a fibrovascular niche at leading edges•Distinct ligand-receptor and spatial niche associations for tumor and stromal cells.•Subpopulation essential tumorigenic gene networks defined by in vivo CRISPR screening
Integration of high-dimensional multi-omics approaches to characterize human cutaneous squamous cell carcinoma identifies a tumor-specific keratinocyte population as well as the immune infiltrates and heterogeneity at tumor leading edges.
We describe a case of chronic coronavirus disease 2019 (COVID-19) in a patient with lymphoma and associated B-cell immunodeficiency. Viral cultures and sequence analysis demonstrate ongoing ...replication of infectious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for at least 119 days. The patient had 3 admissions related to COVID-19 over a 4-month period and was treated twice with remdesivir and convalescent plasma with resolution of symptoms. The patient's lack of seroconversion and prolonged course illustrate the importance of humoral immunity in resolving SARS-CoV-2 infection. This case highlights challenges in managing immunocompromised hosts, who may act as persistent shedders and sources of transmission.
Here, we present Perturb-ATAC, a method that combines multiplexed CRISPR interference or knockout with genome-wide chromatin accessibility profiling in single cells based on the simultaneous ...detection of CRISPR guide RNAs and open chromatin sites by assay of transposase-accessible chromatin with sequencing (ATAC-seq). We applied Perturb-ATAC to transcription factors (TFs), chromatin-modifying factors, and noncoding RNAs (ncRNAs) in ∼4,300 single cells, encompassing more than 63 genotype-phenotype relationships. Perturb-ATAC in human B lymphocytes uncovered regulators of chromatin accessibility, TF occupancy, and nucleosome positioning and identified a hierarchy of TFs that govern B cell state, variation, and disease-associated cis-regulatory elements. Perturb-ATAC in primary human epidermal cells revealed three sequential modules of cis-elements that specify keratinocyte fate. Combinatorial deletion of all pairs of these TFs uncovered their epistatic relationships and highlighted genomic co-localization as a basis for synergistic interactions. Thus, Perturb-ATAC is a powerful strategy to dissect gene regulatory networks in development and disease.
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•A method to measure CRISPR perturbations and chromatin state in single cells•Mapping of dynamic chromatin regulatory networks through intercellular variation•Elucidating principles of epistatic interaction between trans-factors•Perturb-ATAC screen of TF function in epidermal differentiation trajectories
Perturb-ATAC combines CRISPR screening with chromatin accessibility profiling of single cells to uncover regulators of chromatin architecture and regulator occupancy and to determine epistatic relationships between regulatory factors in cell fate decisions.
IntroductionThe Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were ...both published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to develop an extension to the TRIPOD statement (TRIPOD-artificial intelligence, AI) and the PROBAST (PROBAST-AI) tool for prediction model studies that applied machine learning techniques.Methods and analysisTRIPOD-AI and PROBAST-AI will be developed following published guidance from the EQUATOR Network, and will comprise five stages. Stage 1 will comprise two systematic reviews (across all medical fields and specifically in oncology) to examine the quality of reporting in published machine-learning-based prediction model studies. In stage 2, we will consult a diverse group of key stakeholders using a Delphi process to identify items to be considered for inclusion in TRIPOD-AI and PROBAST-AI. Stage 3 will be virtual consensus meetings to consolidate and prioritise key items to be included in TRIPOD-AI and PROBAST-AI. Stage 4 will involve developing the TRIPOD-AI checklist and the PROBAST-AI tool, and writing the accompanying explanation and elaboration papers. In the final stage, stage 5, we will disseminate TRIPOD-AI and PROBAST-AI via journals, conferences, blogs, websites (including TRIPOD, PROBAST and EQUATOR Network) and social media. TRIPOD-AI will provide researchers working on prediction model studies based on machine learning with a reporting guideline that can help them report key details that readers need to evaluate the study quality and interpret its findings, potentially reducing research waste. We anticipate PROBAST-AI will help researchers, clinicians, systematic reviewers and policymakers critically appraise the design, conduct and analysis of machine learning based prediction model studies, with a robust standardised tool for bias evaluation.Ethics and disseminationEthical approval has been granted by the Central University Research Ethics Committee, University of Oxford on 10-December-2020 (R73034/RE001). Findings from this study will be disseminated through peer-review publications.PROSPERO registration numberCRD42019140361 and CRD42019161764.
Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer
. Here we use a systematic approach to study spatial genome integrity in situ and ...describe previously unidentified clonal relationships. We used spatially resolved transcriptomics
to infer spatial copy number variations in >120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.
Drug resistance invariably limits the clinical efficacy of targeted therapy with kinase inhibitors against cancer. Here we show that targeted therapy with BRAF, ALK or EGFR kinase inhibitors induces ...a complex network of secreted signals in drug-stressed human and mouse melanoma and human lung adenocarcinoma cells. This therapy-induced secretome stimulates the outgrowth, dissemination and metastasis of drug-resistant cancer cell clones and supports the survival of drug-sensitive cancer cells, contributing to incomplete tumour regression. The tumour-promoting secretome of melanoma cells treated with the kinase inhibitor vemurafenib is driven by downregulation of the transcription factor FRA1. In situ transcriptome analysis of drug-resistant melanoma cells responding to the regressing tumour microenvironment revealed hyperactivation of several signalling pathways, most prominently the AKT pathway. Dual inhibition of RAF and the PI(3)K/AKT/mTOR intracellular signalling pathways blunted the outgrowth of the drug-resistant cell population in BRAF mutant human melanoma, suggesting this combination therapy as a strategy against tumour relapse. Thus, therapeutic inhibition of oncogenic drivers induces vast secretome changes in drug-sensitive cancer cells, paradoxically establishing a tumour microenvironment that supports the expansion of drug-resistant clones, but is susceptible to combination therapy.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK
Current methods for spatial transcriptomics are limited by low spatial resolution. Here we introduce a method that integrates spatial gene expression data with histological image data from the same ...tissue section to infer higher-resolution expression maps. Using a deep generative model, our method characterizes the transcriptome of micrometer-scale anatomical features and can predict spatial gene expression from histology images alone.
Androgen-receptor (AR) inhibitors, including enzalutamide, are used for treatment of all metastatic castration-resistant prostate cancers (mCRPCs). However, some patients develop resistance or never ...respond. We find that the transcription factor CREB5 confers enzalutamide resistance in an open reading frame (ORF) expression screen and in tumor xenografts. CREB5 overexpression is essential for an enzalutamide-resistant patient-derived organoid. In AR-expressing prostate cancer cells, CREB5 interactions enhance AR activity at a subset of promoters and enhancers upon enzalutamide treatment, including MYC and genes involved in the cell cycle. In mCRPC, we found recurrent amplification and overexpression of CREB5. Our observations identify CREB5 as one mechanism that drives resistance to AR antagonists in prostate cancers.
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•CREB5 promotes resistance to AR inhibitors and androgen therapies in prostate cancer•CREB5 selectively enhances interaction of AR with target genes critical for survival•CREB5 is amplified or overexpressed in therapy-resistant metastatic prostate cancers•Targeting CREB5 is effective in patient-derived models that are therapy resistant
Advanced prostate cancers develop resistance to androgen receptor (AR)-targeting therapies. Hwang et al. show that resistant prostate cancers overexpress or amplify CREB5, mediating resistance to AR inhibition. CREB5 suppression reduces the viability of therapy-resistant patient-derived models, suggesting that CREB5 is a target in prostate cancer patients with limited treatment options.
Alternative splicing of mRNA precursors represents a key gene expression regulatory step and permits the generation of distinct protein products with diverse functions. In a genome-scale expression ...screen for inducers of the epithelial-to-mesenchymal transition (EMT), we found a striking enrichment of RNA-binding proteins. We validated that QKI and RBFOX1 were necessary and sufficient to induce an intermediate mesenchymal cell state and increased tumorigenicity. Using RNA-seq and eCLIP analysis, we found that QKI and RBFOX1 coordinately regulated the splicing and function of the actin-binding protein FLNB, which plays a causal role in the regulation of EMT. Specifically, the skipping of FLNB exon 30 induced EMT by releasing the FOXC1 transcription factor. Moreover, skipping of FLNB exon 30 is strongly associated with EMT gene signatures in basal-like breast cancer patient samples. These observations identify a specific dysregulation of splicing, which regulates tumor cell plasticity and is frequently observed in human cancer.
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