Semi-supervised integration of single-cell transcriptomics data

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.

Activation of the transcription factor NFAT5 in the tumor microenvironment enforces CD8+ T cell exhaustion

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 IFNɣ 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.

T Cell Clonal Analysis Using Single-cell RNA Sequencing and Reference Maps

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.

A CSF-1R-blocking antibody/IL-10 fusion protein increases anti-tumor immunity by effectuating tumor-resident CD8+ T cells

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.

Orthogonal cytokine engineering enables novel synthetic effector states escaping canonical exhaustion in tumor-rejecting CD8+ T cells

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-2Rβγ 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.

Metacells untangle large and complex single-cell transcriptome networks

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04861-1.

A CD4+ T cell reference map delineates subtype-specific adaptation during acute and chronic viral infections

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.

Dissecting the treatment-naive ecosystem of human melanoma brain metastasis

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.

Abstract 984: Dissecting the ecosystem of treatment-naïve melanoma brain metastasis using multi-modal single-cell analysis

Brain metastases are the most frequent malignancies in the brain and are associated with significant morbidity and mortality. Melanoma brain metastases (MBM) occur in most patients with advanced melanoma and are challenging to treat. Our understanding of the treatment-naïve landscape of MBM is still rudimentary, and there are no site-specific molecular therapies available. To gain comprehensive insights into the niche-specific biology of MBM, we performed multi-modal profiling of fresh and frozen samples using single-cell RNA-seq, single-cell TCR-seq, single-nuclei RNA-seq, and spatial transcriptional profiling. We evolved single-nucleus RNA-seq processing methods to enable profiling of minute amounts of archival, frozen specimens and compared data quality and structure between matched fresh and frozen MBM. We curated a treatment-naïve single-transcriptome atlas of MBM, collected either fresh samples over 1 year or profiled frozen samples dating back more than 15 years, and compared these samples to extracranial melanoma metastases (ECMM). In total, we profiled 25 samples with more than 114,000 transcriptomes. We identified more than 20 different cell types, including diverse tumor-infiltrating T-cell subsets and rare dendritic cell types, and tissue-specific cell types, such as activated microglia. Tumor cells in MBM showed an increase in copy number alterations (CNAs) compared to ECMM, which we validated using an external dataset of whole exome sequencing (WES) data including both MBM and ECMM. MBM-derived tumor cells show enrichment of genes involved in neuronal development and function, and site-specific metabolic programs (e.g., oxidative phosphorylation). Comparison with an external bulk RNA-seq dataset validated enriched key genes in MBM and ECMM as putative dependencies. We recovered cell-cell interactions between tumor and brain-resident cells involved in brain development, homeostasis, and disease. Similar to ECMM, the tumor microenvironment of MBM contained CD8+ T cells across a spectrum of differentiation, exhaustion and expansion, which was associated with loss of TCF7 expression and adoption of a TOX+ cell state. CD4+ T cells included T regulatory, T helper and T follicular-helper-like expression profiles. Plasma cells showed spatially localized expansion and limited heterogeneity. Myeloid cells largely adopted pro-tumorigenic cell states, including microglia, the brain-resident myeloid cells, which showed an activation trajectory characterized by expression of SPP1 (osteopontin). Spatial transcriptional analysis revealed restricted expression of antigen presentation genes with only a subset of these locations showing a type I interferon response. In summary, this work presents a multi-modal single-cell approach to dissect and compare the landscape of treatment-naïve MBM and ECMM.

Citation Format: Johannes C. Melms, Jana Biermann, Amit Dipak Amin, Yiping Wang, Somnath Tagore, Massimo Andreatta, Ajay Nair, Meri Rogava, Patricia Ho, Lindsay A. Caprio, Zachary H. Walsh, Shivem Shah, Daniel H. Vacarro, Blake Caldwell, Adrienne M. Luoma, Joseph Driver, Matthew Ingham, Suthee Rapisuwon, Jennifer Wargo, Craig L. Slinguff, Evan Z. Macosco, Fei Chen, Richard Carvajal, Michael B. Atkins, Michael A. Davies, Elham Azizi, Santiago J. Carmona, Hanina Hibshoosh, Peter D. Canoll, Jeffrey N. Bruce, Wenya L. Bi, Gary K. Schwartz, Benjamin Izar. Dissecting the ecosystem of treatment-naïve melanoma brain metastasis using multi-modal single-cell analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 984.

scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets

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.

Characterization of ADAT2/3 molecules in Trypanosoma cruzi and regulation of mucin gene expression by tRNA editing

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.

Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy

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.

Myeloid antigen-presenting cell niches sustain antitumor T cells and license PD-1 blockade via CD28 costimulation

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.

SPICA: Swiss portal for immune cell analysis

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.

APRANK: Computational Prioritization of Antigenic Proteins and Peptides From Complete Pathogen Proteomes

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.

UCell: Robust and scalable single-cell gene signature scoring

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.

STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data

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.

Interpretation of T cell states from single-cell transcriptomics data using reference atlases

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.

Prediction of neo-epitope immunogenicity reveals TCR recognition determinants and provides insight into immunoediting

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.

Deciphering the transcriptomic landscape of tumor-infiltrating CD8 lymphocytes in B16 melanoma tumors with single-cell RNA-Seq

Recent studies have proposed that tumor-specific tumor-infiltrating CD8⁺ T lymphocytes (CD8 TIL) can be classified into two main groups: "exhausted" TILs, characterized by high expression of the inhibitory receptors PD-1 and TIM-3 and lack of transcription factor 1 (Tcf1); and "memory-like" TILs, with self-renewal capacity and co-expressing Tcf1 and PD-1. However, a comprehensive definition of the heterogeneity existing within CD8 TILs has yet to be clearly established. To investigate this heterogeneity at the transcriptomic level, we performed paired single-cell RNA and TCR sequencing of CD8 T cells infiltrating B16 murine melanoma tumors, including cells of known tumor specificity. Unsupervised clustering and gene-signature analysis revealed four distinct CD8 TIL states - exhausted, memory-like, naïve and effector memory-like (EM-like) - and predicted novel markers, including Ly6C for the EM-like cells, that were validated by flow cytometry. Tumor-specific PMEL T cells were predominantly found within the exhausted and memory-like states but also within the EM-like state. Further, T cell receptor sequencing revealed a large clonal expansion of exhausted, memory-like and EM-like cells with partial clonal relatedness between them. Finally, meta-analyses of public bulk and single-cell RNA-seq data suggested that anti-PD-1 treatment induces the expansion of EM-like cells. Our reference map of the transcriptomic landscape of murine CD8 TILs will help interpreting future bulk and single-cell transcriptomic studies and may guide the analysis of CD8IL subpopulations in response to therapeutic interventions.

Enhanced Phenotype Definition for Precision Isolation of Precursor Exhausted Tumor-Infiltrating CD8 T Cells

In the context of adoptive T cell transfer (ACT) for cancer treatment, it is crucial to generate in vitro large amounts of tumor-specific CD8 T cells with high potential to persist in vivo. PD-1, Tim3, and CD39 have been proposed as markers of tumor-specific tumor-infiltrating CD8 T lymphocytes (CD8 TILs). However, these molecules are highly expressed by terminally differentiated exhausted CD8 T cells (Tex) that lack proliferation potential. Therefore, optimized strategies to isolate tumor-specific TILs with high proliferative potential, such as Tcf1+ precursor exhausted T cells (Tpe) are needed to improve in vivo persistence of ACT. Here we aimed at defining cell surface markers that would unequivocally identify Types for precision cell sorting increasing the purity of tumor-specific PD-1+ Tcf1+ Tpe from total TILs. Transcriptomic analysis of Tpe vs. Tex CD8 TIL subsets from B16 tumors and primary human melanoma tumors revealed that Tpes are enriched in Slamf6 and lack Entpd1 and Havcr2 expression, which encode Slamf6, CD39, and Tim3 cell surface proteins, respectively. Indeed, we observed by flow cytometry that CD39- Tim3- Slamf6+ PD-1+ cells yielded maximum enrichment for tumor specific PD-1+ Tcf1+ OT1 TILs in B16.OVA tumors. Moreover, this population showed higher re-expansion capacity upon an acute infection recall response compared to the CD39+ counterparts or bulk PD-1+ TILs. Hence, we report an enhanced sorting strategy (CD39- Tim3- Slamf6+ PD-1+) of Tpes. In conclusion, we show that optimization of CD8 TIL cell sorting strategy is a viable approach to improve recall capacity and in vivo persistence of transferred cells in the context of ACT.

MicroRNA-155 Expression Is Enhanced by T-cell Receptor Stimulation Strength and Correlates with Improved Tumor Control in Melanoma

microRNAs are short noncoding RNAs that regulate protein expression posttranscriptionally. We previously showed that miR-155 promotes effector CD8⁺ T-cell responses. However, little is known about the regulation of miR-155 expression. Here, we report that antigen affinity and dose determine miR-155 expression in CD8⁺ T cells. In B16 tumors expressing a low-affinity antigen ligand, tumor-specific infiltrating CD8⁺ T cells showed variable miR-155 expression, whereby high miR-155 expression was associated with more cytokine-producing cells and tumor control. Moreover, anti-PD-1 treatment led to both increased miR-155 expression and tumor control by specific CD8⁺ T cells. In addition, miR-155 overexpression enhanced exhausted CD8⁺ T-cell persistence in the LCMV cl13 chronic viral infection model. In agreement with these observations in mouse models, miR-155 expression in human effector memory CD8⁺ T cells positively correlated with their frequencies in tumor-infiltrated lymph nodes of melanoma patients. Low miR-155 target gene signature in tumors was associated with prolonged overall survival in melanoma patients. Altogether, these results raise the possibility that high miR-155 expression in CD8⁺ tumor-infiltrating T cells may be a surrogate marker of the relative potency of in situ antigen-specific CD8⁺ T-cell responses.

Molecular and antigenic characterization of Trypanosoma cruzi TolT proteins

Background

TolT was originally described as a Trypanosoma cruzi molecule that accumulated on the trypomastigote flagellum bearing similarity to bacterial TolA colicins receptors. Preliminary biochemical studies indicated that TolT resolved in SDS-PAGE as ~3–5 different bands with sizes between 34 and 45 kDa, and that this heterogeneity could be ascribed to differences in polypeptide glycosylation. However, the recurrent identification of TolT-deduced peptides, and variations thereof, in trypomastigote proteomic surveys suggested an intrinsic TolT complexity, and prompted us to undertake a thorough reassessment of this antigen.

Methods/Principle findings

Genome mining exercises showed that TolT constitutes a larger-than-expected family of genes, with at least 12 polymorphic members in the T. cruzi CL Brener reference strain and homologs in different trypanosomes. According to structural features, TolT deduced proteins could be split into three robust groups, termed TolT-A, TolT-B, and TolT-C, all of them showing marginal sequence similarity to bacterial TolA proteins and canonical signatures of surface localization/membrane association, most of which were herein experimentally validated. Further biochemical and microscopy-based characterizations indicated that this grouping may have a functional correlate, as TolT-A, TolT-B and TolT-C molecules showed differences in their expression profile, sub-cellular distribution, post-translational modification(s) and antigenic structure. We finally used a recently developed fluorescence magnetic beads immunoassay to validate a recombinant protein spanning the central and mature region of a TolT-B deduced molecule for Chagas disease serodiagnosis.

Conclusion/Significance

This study unveiled an unexpected genetic and biochemical complexity within the TolT family, which could be exploited for the development of novel T. cruzi biomarkers with diagnostic/therapeutic applications.

Chagas disease, caused by the protozoan Trypanosoma cruzi, is a lifelong and debilitating neglected illness of major significance in Latin America, for which no vaccine or adequate drugs are yet available. Identification of novel biomarkers able to transcend the current limits of diagnostic and/or therapeutic assessment methods hence surfaces as a main priority in Chagas disease applied research. In this framework, we herein undertook a thorough biochemical and antigenic characterization of T. cruzi TolT surface antigens. Our results unveil an unexpected complexity within this family, with at least 12 polymorphic TolT genes in the T. cruzi CL Brener reference strain genome. According to structural features, TolT deduced molecules could be split into three robust groups that show differences in their structural features, expression profile, sub-cellular distribution, post-translational modification(s) and antigenic structure. Overall, we show that TolT molecules are conspicuously expressed by both major mammal-dwelling stages of the parasite, and that they are differentially recognized by the immune system in Chagasic patients and in T. cruzi-infected mammals. Our findings are discussed in terms of the evolution and possible structural/functional roles of TolT molecules, as well as in terms of their applicability in Chagas disease serodiagnosis.

Intratumoral Tcf1+PD-1+CD8+ T Cells with Stem-like Properties Promote Tumor Control in Response to Vaccination and Checkpoint Blockade Immunotherapy

Checkpoint blockade mediates a proliferative response of tumor-infiltrating CD8⁺ T lymphocytes (TILs). The origin of this response has remained elusive because chronic activation promotes terminal differentiation or exhaustion of tumor-specific T cells. Here we identified a subset of tumor-reactive TILs bearing hallmarks of exhausted cells and central memory cells, including expression of the checkpoint protein PD-1 and the transcription factor Tcf1. Tcf1⁺PD-1⁺ TILs mediated the proliferative response to immunotherapy, generating both Tcf1⁺PD-1⁺ and differentiated Tcf1^-PD-1⁺ cells. Ablation of Tcf1⁺PD-1⁺ TILs restricted responses to immunotherapy. Tcf1 was not required for the generation of Tcf1⁺PD-1⁺ TILs but was essential for the stem-like functions of these cells. Human TCF1⁺PD-1⁺ cells were detected among tumor-reactive CD8⁺ T cells in the blood of melanoma patients and among TILs of primary melanomas. Thus, immune checkpoint blockade relies not on reversal of T cell exhaustion programs, but on the proliferation of a stem-like TIL subset.

Novel biotechnological platform based on baculovirus occlusion bodies carrying Babesia bovis small antigenic peptides for the design of a diagnostic enzyme-linked immunosorbent assay (ELISA)

Baculoviruses are large DNA virus of insects principally employed in recombinant protein expression. Its ability to form occlusion bodies (OBs), which are composed mainly of polyhedrin protein (POLH), makes them biotechnologically attractive, as these crystals (polyhedra) can incorporate foreign peptides and can be easily isolated. On the other hand, peptide microarrays allow rapid and inexpensive high-throughput serological screening of new candidates to be incorporated to OBs. To integrate these 2 biotechnological approaches, we worked on Babesia bovis, one of the causative agents of bovine babesiosis. Current molecular diagnosis of infection with B. bovis includes enzyme-linked immunosorbent assay (ELISA) techniques, which use merozoite lysate obtained from infected bovine erythrocytes. However, it is important to produce recombinant antigens that replace the use of crude antigens. Here, we describe a new biotechnological platform for the design of indirect ELISAs based on 5 antigenic peptides of 15 amino acid residues of B. bovis (ApBb), selected from a peptide microarray and expressed as a fusion to POLH. An Sf9POLH_E44G packaging cell line infected with recombinant baculoviruses carrying POLH-ApBb fusions yielded higher levels of chimeric polyhedra, highlighting the advantage of a trans-contribution of a mutant copy of polyhedrin. Finally, the use of dissolved recombinant polyhedra as antigens was successful in an ELISA assay, as B. bovis-positive sera recognized the fusion POLH-ApBb. Thus, the use of this platform resulted in a promising alternative for molecular diagnosis of relevant infectious diseases.

Next-generation ELISA diagnostic assay for Chagas Disease based on the combination of short peptidic epitopes

Chagas Disease, caused by the protozoan Trypanosoma cruzi, is a major health and economic problem in Latin America for which no vaccine or appropriate drugs for large-scale public health interventions are yet available. Accurate diagnosis is essential for the early identification and follow up of vector-borne cases and to prevent transmission of the disease by way of blood transfusions and organ transplantation. Diagnosis is routinely performed using serological methods, some of which require the production of parasite lysates, parasite antigenic fractions or purified recombinant antigens. Although available serological tests give satisfactory results, the production of reliable reagents remains laborious and expensive. Short peptides spanning linear B-cell epitopes have proven ideal serodiagnostic reagents in a wide range of diseases. Recently, we have conducted a large-scale screening of T. cruzi linear B-cell epitopes using high-density peptide chips, leading to the identification of several hundred novel sequence signatures associated to chronic Chagas Disease. Here, we performed a serological assessment of 27 selected epitopes and of their use in a novel multipeptide-based diagnostic method. A combination of 7 of these peptides were finally evaluated in ELISA format against a panel of 199 sera samples (Chagas-positive and negative, including sera from Leishmaniasis-positive subjects). The multipeptide formulation displayed a high diagnostic performance, with a sensitivity of 96.3% and a specificity of 99.15%. Therefore, the use of synthetic peptides as diagnostic tools are an attractive alternative in Chagas’ disease diagnosis.

Chagas disease, caused by the parasite Trypanosoma cruzi, is a life-long and debilitating illness of major significance throughout Latin America, and an emergent threat to global public health. Diagnostic tests are key tools to support disease surveillance, and to ultimately help stop transmission of the parasite. However currently available diagnostic methods have several limitations. Identification of novel biomarkers with improved diagnostic characteristics is a main priority. Recently, we conducted a large-scale screening looking for new T. cruzi antigens using short peptides displayed on a solid support at high-density. This led to the identification of several hundred novel antigenic epitopes. In this work we validated the serodiagnostic performance of 27 of these against an extended panel of human serum samples. Based on this analysis, we developed a proof-of-principle multiplex diagnostic kit by combining different validated reactive peptides. Overall, our data support the applicability of high-density peptide microarrays for the rapid identification and mapping epitopes that could be readily translated into novel and useful tools for diagnosis of Chagas disease.

High-resolution profiling of linear B-cell epitopes from mucin-associated surface proteins (MASPs) of Trypanosoma cruzi during human infections

BACKGROUND

The Trypanosoma cruzi genome bears a huge family of genes and pseudogenes coding for Mucin-Associated Surface Proteins (MASPs). MASP molecules display a 'mosaic' structure, with highly conserved flanking regions and a strikingly variable central and mature domain made up of different combinations of a large repertoire of short sequence motifs. MASP molecules are highly expressed in mammal-dwelling stages of T. cruzi and may be involved in parasite-host interactions and/or in diverting the immune response.

METHODS/PRINCIPLE FINDINGS

High-density microarrays composed of fully overlapped 15mer peptides spanning the entire sequences of 232 non-redundant MASPs (~25% of the total MASP content) were screened with chronic Chagasic sera. This strategy led to the identification of 86 antigenic motifs, each one likely representing a single linear B-cell epitope, which were mapped to 69 different MASPs. These motifs could be further grouped into 31 clusters of structurally- and likely antigenically-related sequences, and fully characterized. In contrast to previous reports, we show that MASP antigenic motifs are restricted to the central and mature region of MASP polypeptides, consistent with their intracellular processing. The antigenicity of these motifs displayed significant positive correlation with their genome dosage and their relative position within the MASP polypeptide. In addition, we verified the biased genetic co-occurrence of certain antigenic motifs within MASP polypeptides, compatible with proposed intra-family recombination events underlying the evolution of their coding genes. Sequences spanning 7 MASP antigenic motifs were further evaluated using distinct synthesis/display approaches and a large panel of serum samples. Overall, the serological recognition of MASP antigenic motifs exhibited a remarkable non normal distribution among the T. cruzi seropositive population, thus reducing their applicability in conventional serodiagnosis. As previously observed in in vitro and animal infection models, immune signatures supported the concurrent expression of several MASPs during human infection.

CONCLUSIONS/SIGNIFICANCE

In spite of their conspicuous expression and potential roles in parasite biology, this study constitutes the first unbiased, high-resolution profiling of linear B-cell epitopes from T. cruzi MASPs during human infection.

Identification of innate lymphoid cells in single-cell RNA-Seq data

Innate lymphoid cells (ILCs) consist of natural killer (NK) cells and non-cytotoxic ILCs that are broadly classified into ILC1, ILC2, and ILC3 subtypes. These cells recently emerged as important early effectors of innate immunity for their roles in tissue homeostasis and inflammation. Over the last few years, ILCs have been extensively studied in mouse and human at the functional and molecular level, including gene expression profiling. However, sorting ILCs with flow cytometry for gene expression analysis is a delicate and time-consuming process. Here we propose and validate a novel framework for studying ILCs at the transcriptomic level using single-cell RNA-Seq data. Our approach combines unsupervised clustering and a new cell type classifier trained on mouse ILC gene expression data. We show that this approach can accurately identify different ILCs, especially ILC2 cells, in human lymphocyte single-cell RNA-Seq data. Our new model relies only on genes conserved across vertebrates, thereby making it in principle applicable in any vertebrate species. Considering the rapid increase in throughput of single-cell RNA-Seq technology, our work provides a computational framework for studying ILC2 cells in single-cell transcriptomic data and may help exploring their conservation in distant vertebrate species.

Single-cell transcriptome analysis of fish immune cells provides insight into the evolution of vertebrate immune cell types

The immune system of vertebrate species consists of many different cell types that have distinct functional roles and are subject to different evolutionary pressures. Here, we first analyzed conservation of genes specific for all major immune cell types in human and mouse. Our results revealed higher gene turnover and faster evolution of trans-membrane proteins in NK cells compared with other immune cell types, and especially T cells, but similar conservation of nuclear and cytoplasmic protein coding genes. To validate these findings in a distant vertebrate species, we used single-cell RNA sequencing of lck:GFP cells in zebrafish and obtained the first transcriptome of specific immune cell types in a nonmammalian species. Unsupervised clustering and single-cell TCR locus reconstruction identified three cell populations, T cells, a novel type of NK-like cells, and a smaller population of myeloid-like cells. Differential expression analysis uncovered new immune-cell-specific genes, including novel immunoglobulin-like receptors, and neofunctionalization of recently duplicated paralogs. Evolutionary analyses confirmed the higher gene turnover of trans-membrane proteins in NK cells compared with T cells in fish species, suggesting that this is a general property of immune cell types across all vertebrates.

Towards High-throughput Immunomics for Infectious Diseases: Use of Next-generation Peptide Microarrays for Rapid Discovery and Mapping of Antigenic Determinants

Complete characterization of antibody specificities associated to natural infections is expected to provide a rich source of serologic biomarkers with potential applications in molecular diagnosis, follow-up of chemotherapeutic treatments, and prioritization of targets for vaccine development. Here, we developed a highly-multiplexed platform based on next-generation high-density peptide microarrays to map these specificities in Chagas Disease, an exemplar of a human infectious disease caused by the protozoan Trypanosoma cruzi. We designed a high-density peptide microarray containing more than 175,000 overlapping 15mer peptides derived from T. cruzi proteins. Peptides were synthesized in situ on microarray slides, spanning the complete length of 457 parasite proteins with fully overlapped 15mers (1 residue shift). Screening of these slides with antibodies purified from infected patients and healthy donors demonstrated both a high technical reproducibility as well as epitope mapping consistency when compared with earlier low-throughput technologies. Using a conservative signal threshold to classify positive (reactive) peptides we identified 2,031 disease-specific peptides and 97 novel parasite antigens, effectively doubling the number of known antigens and providing a 10-fold increase in the number of fine mapped antigenic determinants for this disease. Finally, further analysis of the chip data showed that optimizing the amount of sequence overlap of displayed peptides can increase the protein space covered in a single chip by at least ∼threefold without sacrificing sensitivity. In conclusion, we show the power of high-density peptide chips for the discovery of pathogen-specific linear B-cell epitopes from clinical samples, thus setting the stage for high-throughput biomarker discovery screenings and proteome-wide studies of immune responses against pathogens.

Characterization of Toxoplasma gondii subtelomeric-like regions: identification of a long-range compositional bias that is also associated with gene-poor regions

BACKGROUND

Chromosome ends are composed of telomeric repeats and subtelomeric regions, which are patchworks of genes interspersed with repeated elements. Although chromosome ends display similar arrangements in different species, their sequences are highly divergent. In addition, these regions display a particular nucleosomal composition and bind specific factors, therefore producing a special kind of heterochromatin. Using data from currently available draft genomes we have characterized these putative Telomeric Associated Sequences in Toxoplasma gondii.

RESULTS

An all-vs-all pairwise comparison of T. gondii assembled chromosomes revealed the presence of conserved regions of ∼ 30 Kb located near the ends of 9 of the 14 chromosomes of the genome of the ME49 strain. Sequence similarity among these regions is ∼ 70%, and they are also highly conserved in the GT1 and VEG strains. However, they are unique to Toxoplasma with no detectable similarity in other Apicomplexan parasites. The internal structure of these sequences consists of 3 repetitive regions separated by high-complexity sequences without annotated genes, except for a gene from the Toxoplasma Specific Family. ChIP-qPCR experiments showed that nucleosomes associated to these sequences are enriched in histone H4 monomethylated at K20 (H4K20me1), and the histone variant H2A.X, suggesting that they are silenced sequences (heterochromatin). A detailed characterization of the base composition of these sequences, led us to identify a strong long-range compositional bias, which was similar to that observed in other genomic silenced fragments such as those containing centromeric sequences, and was negatively correlated to gene density.

CONCLUSIONS

We identified and characterized a region present in most Toxoplasma assembled chromosomes. Based on their location, sequence features, and nucleosomal markers we propose that these might be part of subtelomeric regions of T. gondii. The identified regions display a unique trinucleotide compositional bias, which is shared (despite the lack of any detectable sequence similarity) with other silenced sequences, such as those making up the chromosome centromeres. We also identified other genomic regions with this compositional bias (but no detectable sequence similarity) that might be functionally similar.

Genome-wide analysis of 3'-untranslated regions supports the existence of post-transcriptional regulons controlling gene expression in trypanosomes

In eukaryotic cells, a group of messenger ribonucleic acids (mRNAs) encoding functionally interrelated proteins together with the trans-acting factors that coordinately modulate their expression is termed a post-transcriptional regulon, due to their partial analogy to a prokaryotic polycistron. This mRNA clustering is organized by sequence-specific RNA-binding proteins (RBPs) that bind cis-regulatory elements in the noncoding regions of genes, and mediates the synchronized control of their fate. These recognition motifs are often characterized by conserved sequences and/or RNA structures, and it is likely that various classes of cis-elements remain undiscovered. Current evidence suggests that RNA regulons govern gene expression in trypanosomes, unicellular parasites which mainly use post-transcriptional mechanisms to control protein synthesis. In this study, we used motif discovery tools to test whether groups of functionally related trypanosomatid genes contain a common cis-regulatory element. We obtained conserved structured RNA motifs statistically enriched in the noncoding region of 38 out of 53 groups of metabolically related transcripts in comparison with a random control. These motifs have a hairpin loop structure, a preferred sense orientation and are located in close proximity to the open reading frames. We found that 15 out of these 38 groups represent unique motifs in which most 3'-UTR signature elements were group-specific. Two extensively studied Trypanosoma cruzi RBPs, TcUBP1 and TcRBP3 were found associated with a few candidate RNA regulons. Interestingly, 13 motifs showed a strong correlation with clusters of developmentally co-expressed genes and six RNA elements were enriched in gene clusters affected after hyperosmotic stress. Here we report a systematic genome-wide in silico screen to search for novel RNA-binding sites in transcripts, and describe an organized network of several coordinately regulated cohorts of mRNAs in T. cruzi. Moreover, we found that structured RNA elements are also conserved in other human pathogens. These results support a model of regulation of gene expression by multiple post-transcriptional regulons in trypanosomes.

TDR Targets: a chemogenomics resource for neglected diseases

The TDR Targets Database (http://tdrtargets.org) has been designed and developed as an online resource to facilitate the rapid identification and prioritization of molecular targets for drug development, focusing on pathogens responsible for neglected human diseases. The database integrates pathogen specific genomic information with functional data (e.g. expression, phylogeny, essentiality) for genes collected from various sources, including literature curation. This information can be browsed and queried using an extensive web interface with functionalities for combining, saving, exporting and sharing the query results. Target genes can be ranked and prioritized using numerical weights assigned to the criteria used for querying. In this report we describe recent updates to the TDR Targets database, including the addition of new genomes (specifically helminths), and integration of chemical structure, property and bioactivity information for biological ligands, drugs and inhibitors and cheminformatic tools for querying and visualizing these chemical data. These changes greatly facilitate exploration of linkages (both known and predicted) between genes and small molecules, yielding insight into whether particular proteins may be druggable, effectively allowing the navigation of chemical space in a genomics context.

Identification of attractive drug targets in neglected-disease pathogens using an in silico approach

Background

The increased sequencing of pathogen genomes and the subsequent availability of genome-scale functional datasets are expected to guide the experimental work necessary for target-based drug discovery. However, a major bottleneck in this has been the difficulty of capturing and integrating relevant information in an easily accessible format for identifying and prioritizing potential targets. The open-access resource TDRtargets.org facilitates drug target prioritization for major tropical disease pathogens such as the mycobacteria Mycobacterium leprae and Mycobacterium tuberculosis; the kinetoplastid protozoans Leishmania major, Trypanosoma brucei, and Trypanosoma cruzi; the apicomplexan protozoans Plasmodium falciparum, Plasmodium vivax, and Toxoplasma gondii; and the helminths Brugia malayi and Schistosoma mansoni.

Methodology/Principal Findings

Here we present strategies to prioritize pathogen proteins based on whether their properties meet criteria considered desirable in a drug target. These criteria are based upon both sequence-derived information (e.g., molecular mass) and functional data on expression, essentiality, phenotypes, metabolic pathways, assayability, and druggability. This approach also highlights the fact that data for many relevant criteria are lacking in less-studied pathogens (e.g., helminths), and we demonstrate how this can be partially overcome by mapping data from homologous genes in well-studied organisms. We also show how individual users can easily upload external datasets and integrate them with existing data in TDRtargets.org to generate highly customized ranked lists of potential targets.

Conclusions/Significance

Using the datasets and the tools available in TDRtargets.org, we have generated illustrative lists of potential drug targets in seven tropical disease pathogens. While these lists are broadly consistent with the research community's current interest in certain specific proteins, and suggest novel target candidates that may merit further study, the lists can easily be modified in a user-specific manner, either by adjusting the weights for chosen criteria or by changing the criteria that are included.

In cell-based drug development, researchers attempt to create drugs that kill a pathogen without necessarily understanding the details of how the drugs work. In contrast, target-based drug development entails the search for compounds that act on a specific intracellular target—often a protein known or suspected to be required for survival of the pathogen. The latter approach to drug development has been facilitated greatly by the sequencing of many pathogen genomes and the incorporation of genome data into user-friendly databases. The present paper shows how the database TDRtargets.org can identify proteins that might be considered good drug targets for diseases such as African sleeping sickness, Chagas disease, parasitic worm infections, tuberculosis, and malaria. These proteins may score highly in searches of the database because they are dissimilar to human proteins, are structurally similar to other “druggable” proteins, have functions that are easy to measure, and/or fulfill other criteria. Researchers can use the lists of high-scoring proteins as a basis for deciding which potential drug targets to pursue experimentally.

TcSNP: a database of genetic variation in Trypanosoma cruzi

The TcSNP database (http://snps.tcruzi.org) integrates information on genetic variation (polymorphisms and mutations) for different stocks, strains and isolates of Trypanosoma cruzi, the causative agent of Chagas disease. The database incorporates sequences (genes from the T. cruzi reference genome, mRNAs, ESTs and genomic sequences); multiple sequence alignments obtained from these sequences; and single-nucleotide polymorphisms and small indels identified by scanning these multiple sequence alignments. Information in TcSNP can be readily interrogated to arrive at gene sets, or SNP sets of interest based on a number of attributes. Sequence similarity searches using BLAST are also supported. This first release of TcSNP contains nearly 170 000 high-confidence candidate SNPs, derived from the analysis of annotated coding sequences. As new sequence data become available, TcSNP will incorporate these data, mapping new candidate SNPs onto the reference genome sequences.