RESUMO
CD11b(+) dendritic cells (DCs) seem to be specialized for presenting antigens via major histocompatibility (MHC) class II complexes to stimulate helper T cells, but the genetic and regulatory basis for this is not established. Conditional deletion of Irf4 resulted in loss of CD11b(+) DCs, impaired formation of peptide-MHC class II complexes and defective priming of helper T cells but not of cytotoxic T lymphocyte (CTL) responses. Gene expression and chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) analyses delineated an IRF4-dependent regulatory module that programs enhanced MHC class II antigen presentation. Expression of the transcription factor IRF4 but not of IRF8 restored the ability of IRF4-deficient DCs to efficiently process and present antigen to MHC class II-restricted T cells and promote helper T cell responses. We propose that the evolutionary divergence of IRF4 and IRF8 facilitated the specialization of DC subsets for distinct modes of antigen presentation and priming of helper T cell versus CTL responses.
Assuntos
Apresentação de Antígeno/genética , Células Dendríticas/imunologia , Antígenos de Histocompatibilidade Classe II/imunologia , Fatores Reguladores de Interferon/metabolismo , Linfócitos T Citotóxicos/imunologia , Linfócitos T Auxiliares-Indutores/imunologia , Animais , Diferenciação Celular/genética , Diferenciação Celular/imunologia , Células Cultivadas , Perfilação da Expressão Gênica , Regulação da Expressão Gênica/genética , Regulação da Expressão Gênica/imunologia , Antígenos de Histocompatibilidade Classe II/genética , Fatores Reguladores de Interferon/genética , Ativação Linfocitária/genética , Camundongos , Camundongos Endogâmicos C57BL , Camundongos Knockout , Ligação Proteica/genética , Transgenes/genéticaRESUMO
Select humans and animals control persistent viral infections via adaptive immune responses that include production of neutralizing antibodies. The precise genetic basis for the control remains enigmatic. Here, we report positional cloning of the gene responsible for production of retrovirus-neutralizing antibodies in mice of the I/LnJ strain. It encodes the beta subunit of the non-classical major histocompatibility complex class II (MHC-II)-like molecule H2-O, a negative regulator of antigen presentation. The recessive and functionally null I/LnJ H2-Ob allele supported the production of virus-neutralizing antibodies independently of the classical MHC haplotype. Subsequent bioinformatics and functional analyses of the human H2-Ob homolog, HLA-DOB, revealed both loss- and gain-of-function alleles, which could affect the ability of their carriers to control infections with human hepatitis B (HBV) and C (HCV) viruses. Thus, understanding of the previously unappreciated role of H2-O (HLA-DO) in immunity to infections may suggest new approaches in achieving neutralizing immunity to viruses.
Assuntos
Anticorpos Neutralizantes , Antígenos HLA-D/metabolismo , Antígenos de Histocompatibilidade Classe II/metabolismo , Imunidade Humoral , Vírus do Tumor Mamário do Camundongo/imunologia , Vírus Rauscher/imunologia , Infecções por Retroviridae/imunologia , Animais , Anticorpos Neutralizantes/metabolismo , Anticorpos Antivirais/metabolismo , Apresentação de Antígeno/genética , Biologia Computacional , Feminino , Predisposição Genética para Doença , Antígenos HLA-D/genética , Células HeLa , Hepatite B/imunologia , Hepatite B/transmissão , Hepatite C/imunologia , Hepatite C/transmissão , Antígenos de Histocompatibilidade Classe II/genética , Humanos , Imunidade Humoral/genética , Masculino , Camundongos , Camundongos Endogâmicos , Camundongos Knockout , Mutação/genética , Polimorfismo Genético , Infecções por Retroviridae/transmissãoRESUMO
Type 2 innate lymphoid cells (ILC2 cells) participate in host defense against helminth parasites and in allergic inflammation. Given their functional relatedness to type 2 helper T cells (T(H)2 cells), we explored whether Gfi1 acts as a shared transcriptional determinant in ILC2 cells. Gfi1 promoted the development of ILC2 cells and controlled their responsiveness during infection with Nippostrongylus brasiliensis and protease allergen-induced lung inflammation. Gfi1 'preferentially' regulated the responsiveness of ILC2 cells to interleukin 33 (IL-33) by directly activating Il1rl1, which encodes the IL-33 receptor (ST2). Loss of Gfi1 in activated ILC2 cells resulted in impaired expression of the transcription factor GATA-3 and a dysregulated genome-wide effector state characterized by coexpression of IL-13 and IL-17. Our findings establish Gfi1 as a shared determinant that reciprocally regulates the type 2 and IL-17 effector states in cells of the innate and adaptive immune systems.
Assuntos
Proteínas de Ligação a DNA/imunologia , Imunidade Inata/imunologia , Células Th2/imunologia , Fatores de Transcrição/imunologia , Transcriptoma/imunologia , Animais , Líquido da Lavagem Broncoalveolar/química , Líquido da Lavagem Broncoalveolar/imunologia , Células Cultivadas , Proteínas de Ligação a DNA/genética , Proteínas de Ligação a DNA/metabolismo , Citometria de Fluxo , Fator de Transcrição GATA3/genética , Fator de Transcrição GATA3/imunologia , Fator de Transcrição GATA3/metabolismo , Proteínas de Fluorescência Verde/genética , Proteínas de Fluorescência Verde/metabolismo , Proteína 1 Semelhante a Receptor de Interleucina-1 , Interleucina-13/genética , Interleucina-13/imunologia , Interleucina-13/metabolismo , Interleucina-17/genética , Interleucina-17/imunologia , Interleucina-17/metabolismo , Interleucina-33 , Interleucinas/farmacologia , Pulmão/imunologia , Pulmão/metabolismo , Ativação Linfocitária/efeitos dos fármacos , Ativação Linfocitária/imunologia , Camundongos , Camundongos Endogâmicos , Camundongos Knockout , Camundongos Transgênicos , Nippostrongylus/imunologia , Nippostrongylus/fisiologia , Análise de Sequência com Séries de Oligonucleotídeos , Receptores de Interleucina/genética , Receptores de Interleucina/imunologia , Receptores de Interleucina/metabolismo , Reação em Cadeia da Polimerase Via Transcriptase Reversa , Infecções por Strongylida/imunologia , Infecções por Strongylida/parasitologia , Células Th2/metabolismo , Células Th2/parasitologia , Fatores de Transcrição/genética , Fatores de Transcrição/metabolismo , Transcriptoma/genéticaRESUMO
Proximity sequencing (Prox-seq) simultaneously measures gene expression, protein expression and protein complexes on single cells. Using information from dual-antibody binding events, Prox-seq infers surface protein dimers at the single-cell level. Prox-seq provides multi-dimensional phenotyping of single cells in high throughput, and was recently used to track the formation of receptor complexes during cell signaling and discovered a novel interaction between CD9 and CD8 in naïve T cells. The distribution of protein abundance can affect identification of protein complexes in a complicated manner in dual-binding assays like Prox-seq. These effects are difficult to explore with experiments, yet important for accurate quantification of protein complexes. Here, we introduce a physical model of Prox-seq and computationally evaluate several different methods for reducing background noise when quantifying protein complexes. Furthermore, we developed an improved method for analysis of Prox-seq data, which resulted in more accurate and robust quantification of protein complexes. Finally, our Prox-seq model offers a simple way to investigate the behavior of Prox-seq data under various biological conditions and guide users toward selecting the best analysis method for their data.
Assuntos
Comunicação Celular , Sequenciamento de Nucleotídeos em Larga Escala , Sequenciamento de Nucleotídeos em Larga Escala/métodosRESUMO
Reproducibility of results obtained using ribonucleic acid (RNA) data across labs remains a major hurdle in cancer research. Often, molecular predictors trained on one dataset cannot be applied to another due to differences in RNA library preparation and quantification, which inhibits the validation of predictors across labs. While current RNA correction algorithms reduce these differences, they require simultaneous access to patient-level data from all datasets, which necessitates the sharing of training data for predictors when sharing predictors. Here, we describe SpinAdapt, an unsupervised RNA correction algorithm that enables the transfer of molecular models without requiring access to patient-level data. It computes data corrections only via aggregate statistics of each dataset, thereby maintaining patient data privacy. Despite an inherent trade-off between privacy and performance, SpinAdapt outperforms current correction methods, like Seurat and ComBat, on publicly available cancer studies, including TCGA and ICGC. Furthermore, SpinAdapt can correct new samples, thereby enabling unbiased evaluation on validation cohorts. We expect this novel correction paradigm to enhance research reproducibility and to preserve patient privacy.
Assuntos
Confidencialidade , Privacidade , Algoritmos , Humanos , RNA , Reprodutibilidade dos TestesRESUMO
Gender bias and the role of sex hormones in autoimmune diseases are well established. In specific pathogen-free nonobese diabetic (NOD) mice, females have 1.3-4.4 times higher incidence of type 1 diabetes (T1D). Germ-free (GF) mice lost the gender bias (female-to-male ratio 1.1-1.2). Gut microbiota differed in males and females, a trend reversed by male castration, confirming that androgens influence gut microbiota. Colonization of GF NOD mice with defined microbiota revealed that some, but not all, lineages overrepresented in male mice supported a gender bias in T1D. Although protection of males did not correlate with blood androgen concentration, hormone-supported expansion of selected microbial lineages may work as a positive-feedback mechanism contributing to the sexual dimorphism of autoimmune diseases. Gene-expression analysis suggested pathways involved in protection of males from T1D by microbiota. Our results favor a two-signal model of gender bias, in which hormones and microbes together trigger protective pathways.
Assuntos
Androgênios/metabolismo , Doenças Autoimunes/imunologia , Autoimunidade , Infecções Bacterianas/imunologia , Diabetes Mellitus Tipo 1/imunologia , Diabetes Mellitus Tipo 1/microbiologia , Animais , Autoimunidade/imunologia , Castração , Feminino , Trato Gastrointestinal/imunologia , Trato Gastrointestinal/microbiologia , Interferon gama/biossíntese , Ativação Linfocitária , Linfócitos/imunologia , Macrófagos/imunologia , Masculino , Metagenoma , Camundongos , Camundongos Endogâmicos NOD , Caracteres SexuaisRESUMO
Single cell RNA sequencing (scRNAseq) can be used to infer a temporal ordering of cellular states. Current methods for the inference of cellular trajectories rely on unbiased dimensionality reduction techniques. However, such biologically agnostic ordering can prove difficult for modeling complex developmental or differentiation processes. The cellular heterogeneity of dynamic biological compartments can result in sparse sampling of key intermediate cell states. To overcome these limitations, we develop a supervised machine learning framework, called Pseudocell Tracer, which infers trajectories in pseudospace rather than in pseudotime. The method uses a supervised encoder, trained with adjacent biological information, to project scRNAseq data into a low-dimensional manifold that maps the transcriptional states a cell can occupy. Then a generative adversarial network (GAN) is used to simulate pesudocells at regular intervals along a virtual cell-state axis. We demonstrate the utility of Pseudocell Tracer by modeling B cells undergoing immunoglobulin class switch recombination (CSR) during a prototypic antigen-induced antibody response. Our results revealed an ordering of key transcription factors regulating CSR to the IgG1 isotype, including the concomitant expression of Nfkb1 and Stat6 prior to the upregulation of Bach2 expression. Furthermore, the expression dynamics of genes encoding cytokine receptors suggest a poised IL-4 signaling state that preceeds CSR to the IgG1 isotype.
Assuntos
Linfócitos B/imunologia , Switching de Imunoglobulina/genética , Aprendizado de Máquina Supervisionado , Animais , Linfócitos B/metabolismo , Fatores de Transcrição de Zíper de Leucina Básica/genética , Biologia Computacional , Simulação por Computador , Bases de Dados de Ácidos Nucleicos , Expressão Gênica , Imunoglobulina G/genética , Interleucina-4/imunologia , Camundongos , Camundongos Endogâmicos C57BL , Modelos Imunológicos , Subunidade p50 de NF-kappa B/genética , Redes Neurais de Computação , RNA-Seq/métodos , RNA-Seq/estatística & dados numéricos , Receptores de Citocinas/genética , Recombinação Genética , Fator de Transcrição STAT6/genética , Transdução de Sinais , Análise de Célula Única/métodos , Análise de Célula Única/estatística & dados numéricosRESUMO
HLA molecules of the MHC class II (MHCII) bind and present pathogen-derived peptides for CD4 T cell activation. Peptide loading of MHCII in the endosomes of cells is controlled by the interplay of the nonclassical MHCII molecules, HLA-DM (DM) and HLA-DO (DO). DM catalyzes peptide loading, whereas DO, an MHCII substrate mimic, prevents DM from interacting with MHCII, resulting in an altered MHCII-peptide repertoire and increased MHCII-CLIP. Although the two genes encoding DO (DOA and DOB) are considered nonpolymorphic, there are rare natural variants. Our previous work identified DOB variants that altered DO function. In this study, we show that natural variation in the DOA gene also impacts DO function. Using the 1000 Genomes Project database, we show that â¼98% of individuals express the canonical DOA*0101 allele, and the remaining individuals mostly express DOA*0102, which we found was a gain-of-function allele. Analysis of 25 natural occurring DOα variants, which included the common alleles, identified three null variants and one variant with reduced and nine with increased ability to modulate DM activity. Unexpectedly, several of the variants produced reduced DO protein levels yet efficiently inhibited DM activity. Finally, analysis of associated single-nucleotide polymorphisms genetically linked the DOA*0102 common allele, a gain-of-function variant, with human hepatitis B viral persistence. In contrast, we found that the DOα F114L null allele was linked with viral clearance. Collectively, these studies show that natural variation occurring in the human DOA gene impacts DO function and can be linked to specific outcomes of viral infections.
Assuntos
Antígenos HLA-D/genética , Hepatite B/genética , Antígenos de Histocompatibilidade Classe II/genética , Polimorfismo de Nucleotídeo Único/genética , Alelos , Apresentação de Antígeno/genética , Linhagem Celular Tumoral , Células HeLa , Hepatite B/virologia , Humanos , Peptídeos/genéticaRESUMO
Single-cell genomics offers powerful tools for studying immune cells, which make it possible to observe rare and intermediate cell states that cannot be resolved at the population level. Advances in computer science and single-cell sequencing technology have created a data-driven revolution in immunology. The challenge for immunologists is to harness computing and turn an avalanche of quantitative data into meaningful discovery of immunological principles, predictive models, and strategies for therapeutics. Here, we review the current literature on computational analysis of single-cell RNA-sequencing data and discuss underlying assumptions, methods, and applications in immunology, and highlight important directions for future research.
Assuntos
Alergia e Imunologia/tendências , Genômica , Sistema Imunitário , Imunoterapia/tendências , Análise de Célula Única , Animais , Biologia Computacional , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , Técnicas Imunológicas , Imunoterapia/métodosRESUMO
MicroRNAs (miRNAs) are essential components of gene regulation, but identification of miRNA targets remains a major challenge. Most target prediction and discovery relies on perfect complementarity of the miRNA seed to the 3' untranslated region (UTR). However, it is unclear to what extent miRNAs target sites without seed matches. Here, we performed a transcriptome-wide identification of the endogenous targets of a single miRNA-miR-155-in a genetically controlled manner. We found that approximately 40% of miR-155-dependent Argonaute binding occurs at sites without perfect seed matches. The majority of these noncanonical sites feature extensive complementarity to the miRNA seed with one mismatch. These noncanonical sites confer regulation of gene expression, albeit less potently than canonical sites. Thus, noncanonical miRNA binding sites are widespread, often contain seed-like motifs, and can regulate gene expression, generating a continuum of targeting and regulation.
Assuntos
MicroRNAs/metabolismo , Transcriptoma , Regiões 3' não Traduzidas , Animais , Proteínas Argonautas/genética , Proteínas Argonautas/metabolismo , Sítios de Ligação , Linfócitos T CD4-Positivos/metabolismo , Biologia Computacional , Regulação para Baixo , Perfilação da Expressão Gênica/métodos , Genes Reporter , Células HEK293 , Humanos , Ativação Linfocitária , Camundongos , Camundongos Knockout , MicroRNAs/genética , Motivos de Nucleotídeos , Análise de Sequência com Séries de Oligonucleotídeos , Reação em Cadeia da Polimerase , RNA Mensageiro/metabolismo , TransfecçãoRESUMO
Motivation: High-throughput experimental techniques have produced a large amount of protein-protein interaction (PPI) data, but their coverage is still low and the PPI data is also very noisy. Computational prediction of PPIs can be used to discover new PPIs and identify errors in the experimental PPI data. Results: We present a novel deep learning framework, DPPI, to model and predict PPIs from sequence information alone. Our model efficiently applies a deep, Siamese-like convolutional neural network combined with random projection and data augmentation to predict PPIs, leveraging existing high-quality experimental PPI data and evolutionary information of a protein pair under prediction. Our experimental results show that DPPI outperforms the state-of-the-art methods on several benchmarks in terms of area under precision-recall curve (auPR), and computationally is more efficient. We also show that DPPI is able to predict homodimeric interactions where other methods fail to work accurately, and the effectiveness of DPPI in specific applications such as predicting cytokine-receptor binding affinities. Availability and implementation: Predicting protein-protein interactions through sequence-based deep learning): https://github.com/hashemifar/DPPI/. Supplementary information: Supplementary data are available at Bioinformatics online.
Assuntos
Aprendizado Profundo , Mapeamento de Interação de Proteínas/métodos , Proteínas/metabolismo , Animais , Área Sob a Curva , Humanos , Camundongos , Ligação Proteica , Proteínas/química , SoftwareRESUMO
Motivation: The B-cell receptor enables individual B cells to identify diverse antigens, including bacterial and viral proteins. While advances in RNA-sequencing (RNA-seq) have enabled high throughput profiling of transcript expression in single cells, the unique task of assembling the full-length heavy and light chain sequences from single cell RNA-seq (scRNA-seq) in B cells has been largely unstudied. Results: We developed a new software tool, BASIC, which allows investigators to use scRNA-seq for assembling BCR sequences at single-cell resolution. To demonstrate the utility of our software, we subjected nearly 200 single human B cells to scRNA-seq, assembled the full-length heavy and the light chains, and experimentally confirmed these results by using single-cell primer-based nested PCRs and Sanger sequencing. Availability and Implementation: http://ttic.uchicago.edu/â¼aakhan/BASIC Contact: aakhan@ttic.edu Supplementary Information: Supplementary data are available at Bioinformatics online.
Assuntos
Perfilação da Expressão Gênica/métodos , Receptores de Antígenos de Linfócitos B/genética , Análise de Sequência de RNA/métodos , Análise de Célula Única/métodos , Software , Regulação da Expressão Gênica , HumanosRESUMO
We describe examples of genomic control circuits that underlie developmental transitions and cellular activation states within the immune system. The architectures of simple gene regulatory networks (GRNs) are highlighted to emphasize conservation of regulatory motifs. The regulatory logic and the cell fate dynamics of each simple GRN, the latter revealed by mathematical and computational modeling, are elaborated. This framework is being expanded to enable the assembly and analysis of complex GRNs using genomic, computational, and high-throughput experimental methodologies. The paradigm will provide new insights into immune cell development and function, and into the pathophysiology of autoimmune and inflammatory disorders, as well as immune malignancies.
Assuntos
Regulação da Expressão Gênica , Redes Reguladoras de Genes , Sistema Imunitário/fisiologia , Imunidade/genética , Animais , Sítios de Ligação , Humanos , Modelos Biológicos , Motivos de Nucleotídeos , Ligação Proteica , Sequências Reguladoras de Ácido Nucleico , Fatores de Transcrição/metabolismoRESUMO
Single-cell RNA sequencing (scRNA-seq) enables comprehensive characterization of the micro-evolutionary processes of B cells during an adaptive immune response, capturing features of somatic hypermutation (SHM) and class switch recombination (CSR). Existing phylogenetic approaches for reconstructing B cell evolution have primarily focused on the SHM process alone. Here, we present tree inference of B cell clonal lineages (TRIBAL), an algorithm designed to optimally reconstruct the evolutionary history of B cell clonal lineages undergoing both SHM and CSR from scRNA-seq data. Through simulations, we demonstrate that TRIBAL produces more comprehensive and accurate B cell lineage trees compared to existing methods. Using real-world datasets, TRIBAL successfully recapitulates expected biological trends in a model affinity maturation system while reconstructing evolutionary histories with more parsimonious class switching than state-of-the-art methods. Thus, TRIBAL significantly improves B cell lineage tracing, useful for modeling vaccine responses, disease progression, and the identification of therapeutic antibodies.
Assuntos
Algoritmos , Linfócitos B , Linhagem da Célula , Análise de Célula Única , Linfócitos B/imunologia , Análise de Célula Única/métodos , Linhagem da Célula/genética , Humanos , Filogenia , Hipermutação Somática de Imunoglobulina/genética , Switching de Imunoglobulina/genética , Análise de Sequência de RNA/métodosRESUMO
Autoimmune diseases such as systemic lupus erythematosus (SLE) display a strong female bias. Although sex hormones have been associated with protecting males from autoimmunity, the molecular mechanisms are incompletely understood. Here we report that androgen receptor (AR) expressed in T cells regulates genes involved in T cell activation directly, or indirectly via controlling other transcription factors. T cell-specific deletion of AR in mice leads to T cell activation and enhanced autoimmunity in male mice. Mechanistically, Ptpn22, a phosphatase and negative regulator of T cell receptor signaling, is downregulated in AR-deficient T cells. Moreover, a conserved androgen-response element is found in the regulatory region of Ptpn22 gene, and the mutation of this transcription element in non-obese diabetic mice increases the incidence of spontaneous and inducible diabetes in male mice. Lastly, Ptpn22 deficiency increases the disease severity of male mice in a mouse model of SLE. Our results thus implicate AR-regulated genes such as PTPN22 as potential therapeutic targets for autoimmune diseases.
Assuntos
Androgênios , Autoimunidade , Lúpus Eritematoso Sistêmico , Proteína Tirosina Fosfatase não Receptora Tipo 22 , Receptores Androgênicos , Linfócitos T , Animais , Proteína Tirosina Fosfatase não Receptora Tipo 22/genética , Proteína Tirosina Fosfatase não Receptora Tipo 22/metabolismo , Masculino , Feminino , Receptores Androgênicos/metabolismo , Receptores Androgênicos/genética , Camundongos , Linfócitos T/imunologia , Linfócitos T/metabolismo , Lúpus Eritematoso Sistêmico/imunologia , Lúpus Eritematoso Sistêmico/genética , Androgênios/metabolismo , Camundongos Knockout , Ativação Linfocitária , Camundongos Endogâmicos NOD , Camundongos Endogâmicos C57BL , Modelos Animais de Doenças , Transdução de SinaisRESUMO
Large-scale cancer genomics projects are profiling hundreds of tumors at multiple molecular layers, including copy number, mRNA and miRNA expression, but the mechanistic relationships between these layers are often excluded from computational models. We developed a supervised learning framework for integrating molecular profiles with regulatory sequence information to reveal regulatory programs in cancer, including miRNA-mediated regulation. We applied our approach to 320 glioblastoma profiles and identified key miRNAs and transcription factors as common or subtype-specific drivers of expression changes. We confirmed that predicted gene expression signatures for proneural subtype regulators were consistent with in vivo expression changes in a PDGF-driven mouse model. We tested two predicted proneural drivers, miR-124 and miR-132, both underexpressed in proneural tumors, by overexpression in neurospheres and observed a partial reversal of corresponding tumor expression changes. Computationally dissecting the role of miRNAs in cancer may ultimately lead to small RNA therapeutics tailored to subtype or individual.
Assuntos
Perfilação da Expressão Gênica , Regulação Neoplásica da Expressão Gênica , Genômica , Glioblastoma/genética , MicroRNAs/metabolismo , Animais , Linhagem Celular Tumoral , Genoma Humano , Humanos , Camundongos , Camundongos Transgênicos , MicroRNAs/genética , Modelos Biológicos , Células-Tronco Neurais/metabolismo , RNA Mensageiro/genética , RNA Mensageiro/metabolismo , Análise de Regressão , Fatores de Transcrição/genéticaRESUMO
B cells are a critical component of the adaptive immune system, responsible for producing antibodies that help protect the body from infections and foreign substances. Single cell RNA-sequencing (scRNA-seq) has allowed for both profiling of B cell receptor (BCR) sequences and gene expression. However, understanding the adaptive and evolutionary mechanisms of B cells in response to specific stimuli remains a significant challenge in the field of immunology. We introduce a new method, TRIBAL, which aims to infer the evolutionary history of clonally related B cells from scRNA-seq data. The key insight of TRIBAL is that inclusion of isotype data into the B cell lineage inference problem is valuable for reducing phylogenetic uncertainty that arises when only considering the receptor sequences. Consequently, the TRIBAL inferred B cell lineage trees jointly capture the somatic mutations introduced to the B cell receptor during affinity maturation and isotype transitions during class switch recombination. In addition, TRIBAL infers isotype transition probabilities that are valuable for gaining insight into the dynamics of class switching. Via in silico experiments, we demonstrate that TRIBAL infers isotype transition probabilities with the ability to distinguish between direct versus sequential switching in a B cell population. This results in more accurate B cell lineage trees and corresponding ancestral sequence and class switch reconstruction compared to competing methods. Using real-world scRNA-seq datasets, we show that TRIBAL recapitulates expected biological trends in a model affinity maturation system. Furthermore, the B cell lineage trees inferred by TRIBAL were equally plausible for the BCR sequences as those inferred by competing methods but yielded lower entropic partitions for the isotypes of the sequenced B cell. Thus, our method holds the potential to further advance our understanding of vaccine responses, disease progression, and the identification of therapeutic antibodies.
RESUMO
Proximity sequencing (Prox-seq) measures gene expression, protein expression, and protein complexes at the single cell level, using information from dual-antibody binding events and a single cell sequencing readout. Prox-seq provides multi-dimensional phenotyping of single cells and was recently used to track the formation of receptor complexes during inflammatory signaling in macrophages and to discover a new interaction between CD9/CD8 proteins on naïve T cells. The distribution of protein abundance affects identification of protein complexes in a complicated manner in dual-binding assays like Prox-seq. These effects are difficult to explore with experiments, yet important for accurate quantification of protein complexes. Here, we introduce a physical model for protein dimer formation on single cells and computationally evaluate several different methods for reducing background noise when quantifying protein complexes. Furthermore, we developed an improved method for analysis of Prox-seq single-cell data, which resulted in more accurate and robust quantification of protein complexes. Finally, our model offers a simple way to investigate the behavior of Prox-seq under various biological conditions and guide users toward selecting the best analysis method for their data.
RESUMO
Spatial transcriptomics (ST) has enhanced RNA analysis in tissue biopsies, but interpreting these data is challenging without expert input. We present Automated Tissue Alignment and Traversal (ATAT), a novel computational framework designed to enhance ST analysis in the context of multiple and complex tissue architectures and morphologies, such as those found in biopsies of the gastrointestinal tract. ATAT utilizes self-supervised contrastive learning on hematoxylin and eosin (H&E) stained images to automate the alignment and traversal of ST data. This approach addresses a critical gap in current ST analysis methodologies, which rely heavily on manual annotation and pathologist expertise to delineate regions of interest for accurate gene expression modeling. Our framework not only streamlines the alignment of multiple ST samples, but also demonstrates robustness in modeling gene expression transitions across specific regions. Additionally, we highlight the ability of ATAT to traverse complex tissue topologies in real-world cases from various individuals and conditions. Our method successfully elucidates differences in immune infiltration patterns across the intestinal wall, enabling the modeling of transcriptional changes across histological layers. We show that ATAT achieves comparable performance to the state-of-the-art method, while alleviating the burden of manual annotation and enabling alignment of tissue samples with complex morphologies.
RESUMO
Fine-tuning of protein-protein interactions occurs naturally through coevolution, but this process is difficult to recapitulate in the laboratory. We describe a platform for synthetic protein-protein coevolution that can isolate matched pairs of interacting muteins from complex libraries. This large dataset of coevolved complexes drove a systems-level analysis of molecular recognition between Z domain-affibody pairs spanning a wide range of structures, affinities, cross-reactivities, and orthogonalities, and captured a broad spectrum of coevolutionary networks. Furthermore, we harnessed pretrained protein language models to expand, in silico, the amino acid diversity of our coevolution screen, predicting remodeled interfaces beyond the reach of the experimental library. The integration of these approaches provides a means of simulating protein coevolution and generating protein complexes with diverse molecular recognition properties for biotechnology and synthetic biology.