T Cell Epitope Predictions.

Throughout the body, T cells monitor MHC-bound ligands expressed on the surface of essentially all cell types. MHC ligands that trigger a T cell immune response are referred to as T cell epitopes. Identifying such epitopes enables tracking, phenotyping, and stimulating T cells involved in immune responses in infectious disease, allergy, autoimmunity, transplantation, and cancer. The specific T cell epitopes recognized in an individual are determined by genetic factors such as the MHC molecules the individual expresses, in parallel to the individual's environmental exposure history. The complexity and importance of T cell epitope mapping have motivated the development of computational approaches that predict what T cell epitopes are likely to be recognized in a given individual or in a broader population. Such predictions guide experimental epitope mapping studies and enable computational analysis of the immunogenic potential of a given protein sequence region.

Using T Cell Receptor Repertoires to Understand the Principles of Adaptive Immune Recognition.

Adaptive immune recognition is mediated by antigen receptors on B and T cells generated by somatic recombination during lineage development. The high level of diversity resulting from this process posed technical limitations that previously limited the comprehensive analysis of adaptive immune recognition. Advances over the last ten years have produced data and approaches allowing insights into how T cells develop, evolutionary signatures of recombination and selection, and the features of T cell receptors that mediate epitope-specific binding and T cell activation. The size and complexity of these data have necessitated the generation of novel computational and analytical approaches, which are transforming how T cell immunology is conducted. Here we review the development and application of novel biological, theoretical, and computational methods for understanding T cell recognition and discuss the potential for improved models of receptor:antigen interactions.

A Perspective on the Role of Computational Models in Immunology.

This is an exciting time for immunology because the future promises to be replete with exciting new discoveries that can be translated to improve health and treat disease in novel ways. Immunologists are attempting to answer increasingly complex questions concerning phenomena that range from the genetic, molecular, and cellular scales to that of organs, whole animals or humans, and populations of humans and pathogens. An important goal is to understand how the many different components involved interact with each other within and across these scales for immune responses to emerge, and how aberrant regulation of these processes causes disease. To aid this quest, large amounts of data can be collected using high-throughput instrumentation. The nonlinear, cooperative, and stochastic character of the interactions between components of the immune system as well as the overwhelming amounts of data can make it difficult to intuit patterns in the data or a mechanistic understanding of the phenomena being studied. Computational models are increasingly important in confronting and overcoming these challenges. I first describe an iterative paradigm of research that integrates laboratory experiments, clinical data, computational inference, and mechanistic computational models. I then illustrate this paradigm with a few examples from the recent literature that make vivid the power of bringing together diverse types of computational models with experimental and clinical studies to fruitfully interrogate the immune system.

The evolution of computational research in a data-centric world.

Computational data-centric research techniques play a prevalent and multi-disciplinary role in life science research. In the past, scientists in wet labs generated the data, and computational researchers focused on creating tools for the analysis of those data. Computational researchers are now becoming more independent and taking leadership roles within biomedical projects, leveraging the increased availability of public data. We are now able to generate vast amounts of data, and the challenge has shifted from data generation to data analysis. Here we discuss the pitfalls, challenges, and opportunities facing the field of data-centric research in biology. We discuss the evolving perception of computational data-driven research and its rise as an independent domain in biomedical research while also addressing the significant collaborative opportunities that arise from integrating computational research with experimental and translational biology. Additionally, we discuss the future of data-centric research and its applications across various areas of the biomedical field.

Understanding the cell: Future views of structural biology.

Determining the structure and mechanisms of all individual functional modules of cells at high molecular detail has often been seen as equal to understanding how cells work. Recent technical advances have led to a flush of high-resolution structures of various macromolecular machines, but despite this wealth of detailed information, our understanding of cellular function remains incomplete. Here, we discuss present-day limitations of structural biology and highlight novel technologies that may enable us to analyze molecular functions directly inside cells. We predict that the progression toward structural cell biology will involve a shift toward conceptualizing a 4D virtual reality of cells using digital twins. These will capture cellular segments in a highly enriched molecular detail, include dynamic changes, and facilitate simulations of molecular processes, leading to novel and experimentally testable predictions. Transferring biological questions into algorithms that learn from the existing wealth of data and explore novel solutions may ultimately unveil how cells work.

Multi-layered computational gene networks by engineered tristate logics.

So far, biocomputation strictly follows traditional design principles of digital electronics, which could reach their limits when assembling gene circuits of higher complexity. Here, by creating genetic variants of tristate buffers instead of using conventional logic gates as basic signal processing units, we introduce a tristate-based logic synthesis (TriLoS) framework for resource-efficient design of multi-layered gene networks capable of performing complex Boolean calculus within single-cell populations. This sets the stage for simple, modular, and low-interference mapping of various arithmetic logics of interest and an effectively enlarged engineering space within single cells. We not only construct computational gene networks running full adder and full subtractor operations at a cellular level but also describe a treatment paradigm building on programmable cell-based therapeutics, allowing for adjustable and disease-specific drug secretion logics in vivo. This work could foster the evolution of modern biocomputers to progress toward unexplored applications in precision medicine.

Modern microbiology: Embracing complexity through integration across scales.

Microbes were the only form of life on Earth for most of its history, and they still account for the vast majority of life's diversity. They convert rocks to soil, produce much of the oxygen we breathe, remediate our sewage, and sustain agriculture. Microbes are vital to planetary health as they maintain biogeochemical cycles that produce and consume major greenhouse gases and support large food webs. Modern microbiologists analyze nucleic acids, proteins, and metabolites; leverage sophisticated genetic tools, software, and bioinformatic algorithms; and process and integrate complex and heterogeneous datasets so that microbial systems may be harnessed to address contemporary challenges in health, the environment, and basic science. Here, we consider an inevitably incomplete list of emergent themes in our discipline and highlight those that we recognize as the archetypes of its modern era that aim to address the most pressing problems of the 21st century.

The future of rapid and automated single-cell data analysis using reference mapping.

As the number of single-cell datasets continues to grow rapidly, workflows that map new data to well-curated reference atlases offer enormous promise for the biological community. In this perspective, we discuss key computational challenges and opportunities for single-cell reference-mapping algorithms. We discuss how mapping algorithms will enable the integration of diverse datasets across disease states, molecular modalities, genetic perturbations, and diverse species and will eventually replace manual and laborious unsupervised clustering pipelines.

Mapping the transcriptome: Realizing the full potential of spatial data analysis.

RNA sequencing in situ allows for whole-transcriptome characterization at high resolution, while retaining spatial information. These data present an analytical challenge for bioinformatics-how to leverage spatial information effectively? Properties of data with a spatial dimension require special handling, which necessitate a different set of statistical and inferential considerations when compared to non-spatial data. The geographical sciences primarily use spatial data and have developed methods to analye them. Here we discuss the challenges associated with spatial analysis and examine how we can take advantage of practice from the geographical sciences to realize the full potential of spatial information in transcriptomic datasets.

Microbiota-mediated inflammation and antimicrobial defense in the intestine.

The diverse microbial populations constituting the intestinal microbiota promote immune development and differentiation, but because of their complex metabolic requirements and the consequent difficulty culturing them, they remained, until recently, largely uncharacterized and mysterious. In the last decade, deep nucleic acid sequencing platforms, new computational and bioinformatics tools, and full-genome characterization of several hundred commensal bacterial species facilitated studies of the microbiota and revealed that differences in microbiota composition can be associated with inflammatory, metabolic, and infectious diseases, that each human is colonized by a distinct bacterial flora, and that the microbiota can be manipulated to reduce and even cure some diseases. Different bacterial species induce distinct immune cell populations that can play pro- and anti-inflammatory roles, and thus the composition of the microbiota determines, in part, the level of resistance to infection and susceptibility to inflammatory diseases. This review summarizes recent work characterizing commensal microbes that contribute to the antimicrobial defense/inflammation axis.

Discovering dominant tumor immune archetypes in a pan-cancer census.

Cancers display significant heterogeneity with respect to tissue of origin, driver mutations, and other features of the surrounding tissue. It is likely that individual tumors engage common patterns of the immune system-here "archetypes"-creating prototypical non-destructive tumor immune microenvironments (TMEs) and modulating tumor-targeting. To discover the dominant immune system archetypes, the University of California, San Francisco (UCSF) Immunoprofiler Initiative (IPI) processed 364 individual tumors across 12 cancer types using standardized protocols. Computational clustering of flow cytometry and transcriptomic data obtained from cell sub-compartments uncovered dominant patterns of immune composition across cancers. These archetypes were profound insofar as they also differentiated tumors based upon unique immune and tumor gene-expression patterns. They also partitioned well-established classifications of tumor biology. The IPI resource provides a template for understanding cancer immunity as a collection of dominant patterns of immune organization and provides a rational path forward to learn how to modulate these to improve therapy.

Dual proteome-scale networks reveal cell-specific remodeling of the human interactome.

Thousands of interactions assemble proteins into modules that impart spatial and functional organization to the cellular proteome. Through affinity-purification mass spectrometry, we have created two proteome-scale, cell-line-specific interaction networks. The first, BioPlex 3.0, results from affinity purification of 10,128 human proteins-half the proteome-in 293T cells and includes 118,162 interactions among 14,586 proteins. The second results from 5,522 immunoprecipitations in HCT116 cells. These networks model the interactome whose structure encodes protein function, localization, and complex membership. Comparison across cell lines validates thousands of interactions and reveals extensive customization. Whereas shared interactions reside in core complexes and involve essential proteins, cell-specific interactions link these complexes, "rewiring" subnetworks within each cell's interactome. Interactions covary among proteins of shared function as the proteome remodels to produce each cell's phenotype. Viewable interactively online through BioPlexExplorer, these networks define principles of proteome organization and enable unknown protein characterization.

Generation and transmission of interlineage recombinants in the SARS-CoV-2 pandemic.

We present evidence for multiple independent origins of recombinant SARS-CoV-2 viruses sampled from late 2020 and early 2021 in the United Kingdom. Their genomes carry single-nucleotide polymorphisms and deletions that are characteristic of the B.1.1.7 variant of concern but lack the full complement of lineage-defining mutations. Instead, the remainder of their genomes share contiguous genetic variation with non-B.1.1.7 viruses circulating in the same geographic area at the same time as the recombinants. In four instances, there was evidence for onward transmission of a recombinant-origin virus, including one transmission cluster of 45 sequenced cases over the course of 2 months. The inferred genomic locations of recombination breakpoints suggest that every community-transmitted recombinant virus inherited its spike region from a B.1.1.7 parental virus, consistent with a transmission advantage for B.1.1.7's set of mutations.

The technological landscape and applications of single-cell multi-omics.

Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.

MorphoSeq: Full Single-Cell Transcriptome Dynamics Up to Gastrulation in a Chordate.

Single-cell RNA sequencing (scRNA-seq) provides a leap forward in resolving cellular diversity and developmental trajectories but fails to comprehensively delineate the spatial organization and precise cellular makeup of individual embryos. Here, we reconstruct from scRNA-seq and light sheet imaging data a canonical digital embryo that captures the genome-wide gene expression trajectory of every single cell at every cell division in the 18 lineages up to gastrulation in the ascidian Phallusia mammillata. By using high-coverage scRNA-seq, we devise a computational framework that stratifies single cells of individual embryos into cell types without prior knowledge. Unbiased transcriptome data analysis mapped each cell's physical position and lineage history, yielding the complete history of gene expression at the genome-wide level for every single cell in a developing embryo. A comparison of individual embryos reveals both extensive reproducibility between symmetric embryo sides and a large inter-embryonic variability due to small differences in embryogenesis timing.

Large-Scale Topological Changes Restrain Malignant Progression in Colorectal Cancer.

Widespread changes to DNA methylation and chromatin are well documented in cancer, but the fate of higher-order chromosomal structure remains obscure. Here we integrated topological maps for colon tumors and normal colons with epigenetic, transcriptional, and imaging data to characterize alterations to chromatin loops, topologically associated domains, and large-scale compartments. We found that spatial partitioning of the open and closed genome compartments is profoundly compromised in tumors. This reorganization is accompanied by compartment-specific hypomethylation and chromatin changes. Additionally, we identify a compartment at the interface between the canonical A and B compartments that is reorganized in tumors. Remarkably, similar shifts were evident in non-malignant cells that have accumulated excess divisions. Our analyses suggest that these topological changes repress stemness and invasion programs while inducing anti-tumor immunity genes and may therefore restrain malignant progression. Our findings call into question the conventional view that tumor-associated epigenomic alterations are primarily oncogenic.

Oxidative Metabolism Drives Immortalization of Neural Stem Cells during Tumorigenesis.

Metabolic reprogramming is a key feature of many cancers, but how and when it contributes to tumorigenesis remains unclear. Here we demonstrate that metabolic reprogramming induced by mitochondrial fusion can be rate-limiting for immortalization of tumor-initiating cells (TICs) and trigger their irreversible dedication to tumorigenesis. Using single-cell transcriptomics, we find that Drosophila brain tumors contain a rapidly dividing stem cell population defined by upregulation of oxidative phosphorylation (OxPhos). We combine targeted metabolomics and in vivo genetic screening to demonstrate that OxPhos is required for tumor cell immortalization but dispensable in neural stem cells (NSCs) giving rise to tumors. Employing an in vivo NADH/NAD+ sensor, we show that NSCs precisely increase OxPhos during immortalization. Blocking OxPhos or mitochondrial fusion stalls TICs in quiescence and prevents tumorigenesis through impaired NAD+ regeneration. Our work establishes a unique connection between cellular metabolism and immortalization of tumor-initiating cells.

Principles for Integrative Structural Biology Studies.

Integrative structure determination is a powerful approach to modeling the structures of biological systems based on data produced by multiple experimental and theoretical methods, with implications for our understanding of cellular biology and drug discovery. This Primer introduces the theory and methods of integrative approaches, emphasizing the kinds of data that can be effectively included in developing models and using the nuclear pore complex as an example to illustrate the practice and challenges involved. These guidelines are intended to aid the researcher in understanding and applying integrative structural methods to systems of their interest and thus take advantage of this rapidly evolving field.

A Pan-cancer Transcriptome Analysis Reveals Pervasive Regulation through Alternative Promoters.

Most human protein-coding genes are regulated by multiple, distinct promoters, suggesting that the choice of promoter is as important as its level of transcriptional activity. However, while a global change in transcription is recognized as a defining feature of cancer, the contribution of alternative promoters still remains largely unexplored. Here, we infer active promoters using RNA-seq data from 18,468 cancer and normal samples, demonstrating that alternative promoters are a major contributor to context-specific regulation of transcription. We find that promoters are deregulated across tissues, cancer types, and patients, affecting known cancer genes and novel candidates. For genes with independently regulated promoters, we demonstrate that promoter activity provides a more accurate predictor of patient survival than gene expression. Our study suggests that a dynamic landscape of active promoters shapes the cancer transcriptome, opening new diagnostic avenues and opportunities to further explore the interplay of regulatory mechanisms with transcriptional aberrations in cancer.

A Growth-Based Framework for Leaf Shape Development and Diversity.

How do genes modify cellular growth to create morphological diversity? We study this problem in two related plants with differently shaped leaves: Arabidopsis thaliana (simple leaf shape) and Cardamine hirsuta (complex shape with leaflets). We use live imaging, modeling, and genetics to deconstruct these organ-level differences into their cell-level constituents: growth amount, direction, and differentiation. We show that leaf shape depends on the interplay of two growth modes: a conserved organ-wide growth mode that reflects differentiation; and a local, directional mode that involves the patterning of growth foci along the leaf edge. Shape diversity results from the distinct effects of two homeobox genes on these growth modes: SHOOTMERISTEMLESS broadens organ-wide growth relative to edge-patterning, enabling leaflet emergence, while REDUCED COMPLEXITY inhibits growth locally around emerging leaflets, accentuating shape differences created by patterning. We demonstrate the predictivity of our findings by reconstructing key features of C. hirsuta leaf morphology in A. thaliana. VIDEO ABSTRACT.