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Batch effects in single-cell RNA-seq data pose a significant challenge for comparative analyses across samples, individuals, and conditions. Although batch effect correction methods are routinely applied, data integration often leads to overcorrection and can result in the loss of biological variability. In this work we present STACAS, a batch correction method for scRNA-seq that leverages prior knowledge on cell types to preserve biological variability upon integration. Through an open-source benchmark, we show that semi-supervised STACAS outperforms state-of-the-art unsupervised methods, as well as supervised methods such as scANVI and scGen. STACAS scales well to large datasets and is robust to incomplete and imprecise input cell type labels, which are commonly encountered in real-life integration tasks. We argue that the incorporation of prior cell type information should be a common practice in single-cell data integration, and we provide a flexible framework for semi-supervised batch effect correction.
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Perfilação da Expressão Gênica , Análise de Célula Única , Humanos , Análise de Sequência de RNA/métodos , Análise de Célula Única/métodos , Perfilação da Expressão Gênica/métodosRESUMO
The success of chimeric antigen receptor (CAR) T cell therapy in treating several hematopoietic malignancies has been difficult to replicate in solid tumors, in part because of T cell exhaustion and eventually dysfunction. To counter T cell dysfunction in the tumor microenvironment, we metabolically armored CAR T cells by engineering them to secrete interleukin-10 (IL-10). We show that IL-10 CAR T cells preserve intact mitochondrial structure and function in the tumor microenvironment and increase oxidative phosphorylation in a mitochondrial pyruvate carrier-dependent manner. IL-10 secretion promoted proliferation and effector function of CAR T cells, leading to complete regression of established solid tumors and metastatic cancers across several cancer types in syngeneic and xenograft mouse models, including colon cancer, breast cancer, melanoma and pancreatic cancer. IL-10 CAR T cells also induced stem cell-like memory responses in lymphoid organs that imparted durable protection against tumor rechallenge. Our results establish a generalizable approach to counter CAR T cell dysfunction through metabolic armoring, leading to solid tumor eradication and long-lasting immune protection.
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Persistent exposure to antigen during chronic infection or cancer renders T cells dysfunctional. The molecular mechanisms regulating this state of exhaustion are thought to be common in infection and cancer, despite obvious differences in their microenvironments. Here we found that NFAT5, an NFAT family transcription factor that lacks an AP-1 docking site, was highly expressed in exhausted CD8+ T cells in the context of chronic infections and tumors but was selectively required in tumor-induced CD8+ T cell exhaustion. Overexpression of NFAT5 in CD8+ T cells reduced tumor control, while deletion of NFAT5 improved tumor control by promoting the accumulation of tumor-specific CD8+ T cells that had reduced expression of the exhaustion-associated proteins TOX and PD-1 and produced more cytokines, such as IFNÉ£ and TNF, than cells with wild-type levels of NFAT5, specifically in the precursor exhausted PD-1+TCF1+TIM-3-CD8+ T cell population. NFAT5 did not promote T cell exhaustion during chronic infection with clone 13 of lymphocytic choriomeningitis virus. Expression of NFAT5 was induced by TCR triggering, but its transcriptional activity was specific to the tumor microenvironment and required hyperosmolarity. Thus, NFAT5 promoted the exhaustion of CD8+ T cells in a tumor-selective fashion.
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Coriomeningite Linfocítica , Neoplasias , Humanos , Fatores de Transcrição/metabolismo , Linfócitos T CD8-Positivos , Exaustão das Células T , Infecção Persistente , Microambiente Tumoral , Receptor de Morte Celular Programada 1/genética , Receptor de Morte Celular Programada 1/metabolismo , Vírus da Coriomeningite Linfocítica , Neoplasias/metabolismoRESUMO
T cells are endowed with T-cell antigen receptors (TCR) that give them the capacity to recognize specific antigens and mount antigen-specific adaptive immune responses. Because TCR sequences are distinct in each naïve T cell, they serve as molecular barcodes to track T cells with clonal relatedness and shared antigen specificity through proliferation, differentiation, and migration. Single-cell RNA sequencing provides coupled information of TCR sequence and transcriptional state in individual cells, enabling T-cell clonotype-specific analyses. In this protocol, we outline a computational workflow to perform T-cell states and clonal analysis from scRNA-seq data based on the R packages Seurat, ProjecTILs, and scRepertoire. Given a scRNA-seq T-cell dataset with TCR sequence information, cell states are automatically annotated by reference projection using the ProjecTILs method. TCR information is used to track individual clonotypes, assess their clonal expansion, proliferation rates, bias towards specific differentiation states, and the clonal overlap between T-cell subtypes. We provide fully reproducible R code to conduct these analyses and generate useful visualizations that can be adapted for the needs of the protocol user. Key features Computational analysis of paired scRNA-seq and scTCR-seq data Characterizing T-cell functional state by reference-based analysis using ProjecTILs Exploring T-cell clonal structure using scRepertoire Linking T-cell clonality to transcriptomic state to study relationships between clonal expansion and functional phenotype Graphical overview.
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Strategies to increase intratumoral concentrations of an anticancer agent are desirable to optimize its therapeutic potential when said agent is efficacious primarily within a tumor but also have significant systemic side effects. Here, we generate a bifunctional protein by fusing interleukin-10 (IL-10) to a colony-stimulating factor-1 receptor (CSF-1R)-blocking antibody. The fusion protein demonstrates significant antitumor activity in multiple cancer models, especially head and neck cancer. Moreover, this bifunctional protein not only leads to the anticipated reduction in tumor-associated macrophages but also triggers proliferation, activation, and metabolic reprogramming of CD8+ T cells. Furthermore, it extends the clonotype diversity of tumor-infiltrated T cells and shifts the tumor microenvironment (TME) to an immune-active state. This study suggests an efficient strategy for designing immunotherapeutic agents by fusing a potent immunostimulatory molecule to an antibody targeting TME-enriched factors.
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Antineoplásicos , Neoplasias , Humanos , Linfócitos T CD8-Positivos , Interleucina-10/metabolismo , Neoplasias/patologia , Antineoplásicos/farmacologia , Receptores Proteína Tirosina Quinases/metabolismo , Receptores de Fator Estimulador de Colônias/metabolismo , Microambiente TumoralRESUMO
To date, no immunotherapy approaches have managed to fully overcome T-cell exhaustion, which remains a mandatory fate for chronically activated effector cells and a major therapeutic challenge. Understanding how to reprogram CD8+ tumor-infiltrating lymphocytes away from exhausted effector states remains an elusive goal. Our work provides evidence that orthogonal gene engineering of T cells to secrete an interleukin (IL)-2 variant binding the IL-2Rßγ receptor and the alarmin IL-33 reprogrammed adoptively transferred T cells to acquire a novel, synthetic effector state, which deviated from canonical exhaustion and displayed superior effector functions. These cells successfully overcame homeostatic barriers in the host and led-in the absence of lymphodepletion or exogenous cytokine support-to high levels of engraftment and tumor regression. Our work unlocks a new opportunity of rationally engineering synthetic CD8+ T-cell states endowed with the ability to avoid exhaustion and control advanced solid tumors.
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Linfócitos T CD8-Positivos , Imunoterapia Adotiva , Interleucina-2 , Neoplasias Experimentais , Linfócitos T CD8-Positivos/imunologia , Exaustão das Células T , Linfócitos do Interstício Tumoral/imunologia , Interleucina-2/farmacologia , Interleucina-33 , Engenharia de Proteínas , Feminino , Animais , Camundongos , Camundongos Endogâmicos C57BL , Linhagem Celular Tumoral , Neoplasias Experimentais/terapia , Receptor de Morte Celular Programada 1/metabolismoRESUMO
Resumen Introducción: Helicobacter pylori es una bacteria asociada con enfermedades gastroduodenales inflamatorias y neoplásicas. La histopatología es uno de los métodos diagnósticos empleados para su detección, la cual tiene sensibilidad del 90% al 95% cuando hay alta densidad de H. pylori; sin embargo, la bacteria puede pasar desapercibida en infecciones de baja densidad porque la coloración de rutina de hematoxilina y eosina (H&E) no es una tinción específica para su detección y presenta variabilidad interobservador. El objetivo de este estudio fue determinar la utilidad de la tinción complementaria de Giemsa para el diagnóstico de H. pylori en lesiones preneoplásicas en las que la bacteria se encuentra en baja densidad. Materiales y métodos: se realizó un estudio descriptivo retrospectivo y prospectivo que incluyó a 65 pacientes con diagnóstico de lesiones preneoplásicas. Las biopsias gástricas se tiñeron con H&E y Giemsa, y fueron evaluadas por dos patólogos. Resultados: la coloración de Giemsa diagnosticó 20,3% de casos más de infección por H. pylori que la H&E, la mayoría de ellos con baja densidad de la bacteria. No hubo diferencias estadísticamente significativas en el diagnóstico de H. pylori de acuerdo con el tipo de muestra. Conclusión: este estudio encontró que la tinción de Giemsa mejora el diagnóstico histopatológico de H. pylori en pacientes con lesiones preneoplásicas.
Abstract Introduction: Helicobacter pylori is a bacterium associated with inflammatory and neoplastic gastroduodenal diseases. Histopathology is one of the diagnostic methods used for its detection, which has a sensitivity of 90% to 95% when there is a high density of H. pylori; however, the bacterium may be missed in low-density infections because routine hematoxylin and eosin (H&E) staining is not specific for its detection and has interobserver variability. This study aimed to determine the usefulness of complementary Giemsa staining for diagnosing H. pylori in preneoplastic lesions where the bacterium was found in low density. Materials and methods: A retrospective/prospective descriptive study was carried out that included 65 patients diagnosed with preneoplastic lesions. Gastric biopsies were stained with H&E and Giemsa and evaluated by two pathologists. Results: Giemsa staining analyzed 20.3% more cases of H. pylori than H&E, most with a low density of the bacteria. There were no statistically significant differences in the diagnosis of H. pylori according to the sample type. Conclusion: This study found that Giemsa staining improves the histopathological diagnosis of H. pylori in patients with preneoplastic lesions.
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BACKGROUND: Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization. RESULTS: We develop a framework called SuperCell to merge highly similar cells into metacells and perform standard scRNA-seq data analyses at the metacell level. Our systematic benchmarking demonstrates that metacells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, metacells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop. CONCLUSIONS: SuperCell is a framework to build and analyze metacells in a way that efficiently preserves the results of scRNA-seq data analyses while significantly accelerating and facilitating them.
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COVID-19 , Transcriptoma , Análise por Conglomerados , Humanos , Análise de Sequência de RNA/métodos , Análise de Célula Única/métodosRESUMO
CD4+ T cells are critical orchestrators of immune responses against a large variety of pathogens, including viruses. While multiple CD4+ T cell subtypes and their key transcriptional regulators have been identified, there is a lack of consistent definition for CD4+ T cell transcriptional states. In addition, the progressive changes affecting CD4+ T cell subtypes during and after immune responses remain poorly defined. Using single-cell transcriptomics, we characterized the diversity of CD4+ T cells responding to self-resolving and chronic viral infections in mice. We built a comprehensive map of virus-specific CD4+ T cells and their evolution over time, and identified six major cell states consistently observed in acute and chronic infections. During the course of acute infections, T cell composition progressively changed from effector to memory states, with subtype-specific gene modules and kinetics. Conversely, in persistent infections T cells acquired distinct, chronicity-associated programs. By single-cell T cell receptor (TCR) analysis, we characterized the clonal structure of virus-specific CD4+ T cells across individuals. Virus-specific CD4+ T cell responses were essentially private across individuals and most T cells differentiated into both Tfh and Th1 subtypes irrespective of their TCR. Finally, we showed that our CD4+ T cell map can be used as a reference to accurately interpret cell states in external single-cell datasets across tissues and disease models. Overall, this study describes a previously unappreciated level of adaptation of the transcriptional states of CD4+ T cells responding to viruses and provides a new computational resource for CD4+ T cell analysis.
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Linfócitos T , Viroses , Animais , Linfócitos T CD4-Positivos , Camundongos , Receptores de Antígenos de Linfócitos T/genéticaRESUMO
Melanoma brain metastasis (MBM) frequently occurs in patients with advanced melanoma; yet, our understanding of the underlying salient biology is rudimentary. Here, we performed single-cell/nucleus RNA-seq in 22 treatment-naive MBMs and 10 extracranial melanoma metastases (ECMs) and matched spatial single-cell transcriptomics and T cell receptor (TCR)-seq. Cancer cells from MBM were more chromosomally unstable, adopted a neuronal-like cell state, and enriched for spatially variably expressed metabolic pathways. Key observations were validated in independent patient cohorts, patient-derived MBM/ECM xenograft models, RNA/ATAC-seq, proteomics, and multiplexed imaging. Integrated spatial analyses revealed distinct geography of putative cancer immune evasion and evidence for more abundant intra-tumoral B to plasma cell differentiation in lymphoid aggregates in MBM. MBM harbored larger fractions of monocyte-derived macrophages and dysfunctional TOX+CD8+ T cells with distinct expression of immune checkpoints. This work provides comprehensive insights into MBM biology and serves as a foundational resource for further discovery and therapeutic exploration.
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Neoplasias Encefálicas , Melanoma , Neoplasias Encefálicas/tratamento farmacológico , Neoplasias Encefálicas/secundário , Linfócitos T CD8-Positivos/patologia , Ecossistema , Humanos , RNA-SeqRESUMO
SUMMARY: A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. Here, we present scGate, an algorithm that automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. scGate purifies a cell population of interest using a set of markers organized in a hierarchical structure, akin to gating strategies employed in flow cytometry. scGate outperforms state-of-the-art single-cell classifiers and it can be applied to multiple modalities of single-cell data (e.g. RNA-seq, ATAC-seq, CITE-seq). scGate is implemented as an R package and integrated with the Seurat framework, providing an intuitive tool to isolate cell populations of interest from heterogeneous single-cell datasets. AVAILABILITY AND IMPLEMENTATION: scGate is available as an R package at https://github.com/carmonalab/scGate (https://doi.org/10.5281/zenodo.6202614). Several reproducible workflows describing the main functions and usage of the package on different single-cell modalities, as well as the code to reproduce the benchmark, can be found at https://github.com/carmonalab/scGate.demo (https://doi.org/10.5281/zenodo.6202585) and https://github.com/carmonalab/scGate.benchmark. Test data are available at https://doi.org/10.6084/m9.figshare.16826071. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Análise de Célula Única , Software , RNA-Seq , Sequenciamento de Cromatina por Imunoprecipitação , Sequenciamento do ExomaRESUMO
Adenosine-to-inosine conversion at position 34 (A34-to-I) of certain tRNAs is essential for expanding their decoding capacity. This reaction is catalyzed by the adenosine deaminase acting on tRNA (ADAT) complex, which in Eukarya is formed by two subunits: ADAT2 and ADAT3. We herein identified and thoroughly characterized the ADAT molecules from the protozoan pathogen Trypanosoma cruzi, the causative agent of Chagas Disease. TcADAT2 and TcADAT3 spontaneously form a catalytically active complex, as shown by expression in engineered bacteria and/or by the increased ex vivo tRNA A-to-I deamination activity of T. cruzi epimastigotes overexpressing TcADAT subunits. Importantly, enhanced TcADAT2/3 activity in transgenic parasites caused a shift in their in vivo tRNAThrAGU signature, which correlated with significant changes in the expression of the Thr-rich TcSMUG proteins. To our knowledge, this is the first evidence indicating that T. cruzi tRNA editing can be modulated in vivo, in turn post-transcriptionally changing the expression of specific genes. Our findings suggest tRNA editing/availability as a forcible step in controlling gene expression and driving codon adaptation in T. cruzi. Moreover, we unveil certain differences between parasite and mammalian host tRNA editing and processing, such as cytosine-to-uridine conversion at position 32 of tRNAThrAGU in T. cruzi, that may be exploited for the identification of novel druggable targets of intervention.
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Doença de Chagas , Trypanosoma cruzi , Animais , Doença de Chagas/genética , Expressão Gênica , Mamíferos , Mucinas , Processamento Pós-Transcricional do RNA , Trypanosoma cruzi/genéticaRESUMO
Single-cell transcriptomics allows the study of immune cell heterogeneity at an unprecedented level of resolution. The Swiss portal for immune cell analysis (SPICA) is a web resource dedicated to the exploration and analysis of single-cell RNA-seq data of immune cells. In contrast to other single-cell databases, SPICA hosts curated, cell type-specific reference atlases that describe immune cell states at high resolution, and published single-cell datasets analysed in the context of these atlases. Additionally, users can privately analyse their own data in the context of existing atlases and contribute to the SPICA database. SPICA is available at https://spica.unil.ch.
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Bases de Dados Genéticas , Transcriptoma/genética , Regulação da Expressão Gênica/genética , Humanos , RNA-Seq/métodos , Análise de Célula Única/métodos , Transcriptoma/imunologiaRESUMO
Developing strategies to inflame tumors is critical for increasing response to immunotherapy. Here, we report that low-dose radiotherapy (LDRT) of murine tumors promotes T-cell infiltration and enables responsiveness to combinatorial immunotherapy in an IFN-dependent manner. Treatment efficacy relied upon mobilizing both adaptive and innate immunity and depended on both cytotoxic CD4+ and CD8+ T cells. LDRT elicited predominantly CD4+ cells with features of exhausted effector cytotoxic cells, with a subset expressing NKG2D and exhibiting proliferative capacity, as well as a unique subset of activated dendritic cells expressing the NKG2D ligand RAE1. We translated these findings to a phase I clinical trial administering LDRT, low-dose cyclophosphamide, and immune checkpoint blockade to patients with immune-desert tumors. In responsive patients, the combinatorial treatment triggered T-cell infiltration, predominantly of CD4+ cells with Th1 signatures. Our data support the rational combination of LDRT with immunotherapy for effectively treating low T cell-infiltrated tumors. SIGNIFICANCE: Low-dose radiation reprogrammed the tumor microenvironment of tumors with scarce immune infiltration and together with immunotherapy induced simultaneous mobilization of innate and adaptive immunity, predominantly CD4+ effector T cells, to achieve tumor control dependent on NKG2D. The combination induced important responses in patients with metastatic immune-cold tumors.This article is highlighted in the In This Issue feature, p. 1.
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Adenocarcinoma Papilar/radioterapia , Neoplasias Ovarianas/radioterapia , Imunidade Adaptativa , Adenocarcinoma Papilar/imunologia , Animais , Linfócitos T CD4-Positivos , Linfócitos T CD8-Positivos , Modelos Animais de Doenças , Feminino , Humanos , Linfócitos do Interstício Tumoral , Camundongos , Camundongos Endogâmicos C57BL , Neoplasias Ovarianas/imunologia , Dosagem Radioterapêutica , Microambiente TumoralRESUMO
The mechanisms regulating exhaustion of tumor-infiltrating lymphocytes (TIL) and responsiveness to PD-1 blockade remain partly unknown. In human ovarian cancer, we show that tumor-specific CD8+ TIL accumulate in tumor islets, where they engage antigen and upregulate PD-1, which restrains their functions. Intraepithelial PD-1+CD8+ TIL can be, however, polyfunctional. PD-1+ TIL indeed exhibit a continuum of exhaustion states, with variable levels of CD28 costimulation, which is provided by antigen-presenting cells (APC) in intraepithelial tumor myeloid niches. CD28 costimulation is associated with improved effector fitness of exhausted CD8+ TIL and is required for their activation upon PD-1 blockade, which also requires tumor myeloid APC. Exhausted TIL lacking proper CD28 costimulation in situ fail to respond to PD-1 blockade, and their response may be rescued by local CTLA-4 blockade and tumor APC stimulation via CD40L.
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Células Apresentadoras de Antígenos/metabolismo , Antígenos CD28/metabolismo , Inibidores de Checkpoint Imunológico/uso terapêutico , Células Mieloides/metabolismo , Neoplasias/tratamento farmacológico , Nicho de Células-Tronco/genética , Humanos , Inibidores de Checkpoint Imunológico/farmacologia , Neoplasias/imunologiaRESUMO
Availability of highly parallelized immunoassays has renewed interest in the discovery of serology biomarkers for infectious diseases. Protein and peptide microarrays now provide a rapid, high-throughput platform for immunological testing and validation of potential antigens and B-cell epitopes. However, there is still a need for tools to prioritize and select relevant probes when designing these arrays. In this work we describe a computational method called APRANK (Antigenic Protein and Peptide Ranker) which integrates multiple molecular features to prioritize potentially antigenic proteins and peptides in a given pathogen proteome. These features include subcellular localization, presence of repetitive motifs, natively disordered regions, secondary structure, transmembrane spans and predicted interaction with the immune system. We trained and tested this method with a number of bacteria and protozoa causing human diseases: Borrelia burgdorferi (Lyme disease), Brucella melitensis (Brucellosis), Coxiella burnetii (Q fever), Escherichia coli (Gastroenteritis), Francisella tularensis (Tularemia), Leishmania braziliensis (Leishmaniasis), Leptospira interrogans (Leptospirosis), Mycobacterium leprae (Leprae), Mycobacterium tuberculosis (Tuberculosis), Plasmodium falciparum (Malaria), Porphyromonas gingivalis (Periodontal disease), Staphylococcus aureus (Bacteremia), Streptococcus pyogenes (Group A Streptococcal infections), Toxoplasma gondii (Toxoplasmosis) and Trypanosoma cruzi (Chagas Disease). We have evaluated this integrative method using non-parametric ROC-curves and made an unbiased validation using Onchocerca volvulus as an independent data set. We found that APRANK is successful in predicting antigenicity for all pathogen species tested, facilitating the production of antigen-enriched protein subsets. We make APRANK available to facilitate the identification of novel diagnostic antigens in infectious diseases.
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Antígenos/análise , Antígenos/imunologia , Simulação por Computador , Infecções/imunologia , Biologia Computacional/métodos , Humanos , ProteomaRESUMO
UCell is an R package for evaluating gene signatures in single-cell datasets. UCell signature scores, based on the Mann-Whitney U statistic, are robust to dataset size and heterogeneity, and their calculation demands less computing time and memory than other available methods, enabling the processing of large datasets in a few minutes even on machines with limited computing power. UCell can be applied to any single-cell data matrix, and includes functions to directly interact with Seurat objects. The UCell package and documentation are available on GitHub at https://github.com/carmonalab/UCell.
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Single-cell RNA sequencing (scRNA-seq) has revealed an unprecedented degree of immune cell diversity. However, consistent definition of cell subtypes and cell states across studies and diseases remains a major challenge. Here we generate reference T cell atlases for cancer and viral infection by multi-study integration, and develop ProjecTILs, an algorithm for reference atlas projection. In contrast to other methods, ProjecTILs allows not only accurate embedding of new scRNA-seq data into a reference without altering its structure, but also characterizing previously unknown cell states that "deviate" from the reference. ProjecTILs accurately predicts the effects of cell perturbations and identifies gene programs that are altered in different conditions and tissues. A meta-analysis of tumor-infiltrating T cells from several cohorts reveals a strong conservation of T cell subtypes between human and mouse, providing a consistent basis to describe T cell heterogeneity across studies, diseases, and species.
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Neoplasias/imunologia , RNA-Seq/métodos , Análise de Célula Única/métodos , Linfócitos T/imunologia , Viroses/imunologia , Animais , Diferenciação Celular/imunologia , Estudos de Coortes , Modelos Animais de Doenças , Regulação da Expressão Gênica/imunologia , Humanos , Linfócitos do Interstício Tumoral/imunologia , Camundongos , Neoplasias/sangue , Neoplasias/patologia , Valores de Referência , Software , Especificidade da Espécie , Subpopulações de Linfócitos T/imunologia , Microambiente Tumoral/imunologia , Viroses/sangueRESUMO
CD8+ T cell recognition of peptide epitopes plays a central role in immune responses against pathogens and tumors. However, the rules that govern which peptides are truly recognized by existing T cell receptors (TCRs) remain poorly understood, precluding accurate predictions of neo-epitopes for cancer immunotherapy. Here, we capitalize on recent (neo-)epitope data to train a predictor of immunogenic epitopes (PRIME), which captures molecular properties of both antigen presentation and TCR recognition. PRIME not only improves prioritization of neo-epitopes but also correlates with T cell potency and unravels biophysical determinants of TCR recognition that we experimentally validate. Analysis of cancer genomics data reveals that recurrent mutations tend to be less frequent in patients where they are predicted to be immunogenic, providing further evidence for immunoediting in human cancer. PRIME will facilitate identification of pathogen epitopes in infectious diseases and neo-epitopes in cancer immunotherapy.
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Apresentação de Antígeno/imunologia , Epitopos de Linfócito T/imunologia , Neoplasias/imunologia , Receptores de Antígenos de Linfócitos T/imunologia , Linfócitos T CD8-Positivos/imunologia , Epitopos de Linfócito T/genética , Humanos , Imunoterapia/métodos , Peptídeos/imunologiaRESUMO
SUMMARY: STACAS is a computational method for the identification of integration anchors in the Seurat environment, optimized for the integration of single-cell (sc) RNA-seq datasets that share only a subset of cell types. We demonstrate that by (i) correcting batch effects while preserving relevant biological variability across datasets, (ii) filtering aberrant integration anchors with a quantitative distance measure and (iii) constructing optimal guide trees for integration, STACAS can accurately align scRNA-seq datasets composed of only partially overlapping cell populations. AVAILABILITY AND IMPLEMENTATION: Source code and R package available at https://github.com/carmonalab/STACAS; Docker image available at https://hub.docker.com/repository/docker/mandrea1/stacas_demo.