FLOW-MAP: a graph-based, force-directed layout algorithm for trajectory mapping in single-cell time course datasets

GTF2I dosage regulates neuronal differentiation and social behavior in 7q11.23 neurodevelopmental disorders

Copy number variations at 7q11.23 cause neurodevelopmental disorders with shared and opposite manifestations. Deletion causes Williams-Beuren syndrome featuring hypersociability, while duplication causes 7q11.23 microduplication syndrome (7Dup), frequently exhibiting autism spectrum disorder (ASD). Converging evidence indicates GTF2I as key mediator of the cognitive-behavioral phenotypes, yet its role in cortical development and behavioral hallmarks remains largely unknown. We integrated proteomic and transcriptomic profiling of patient-derived cortical organoids, including longitudinally at single-cell resolution, to dissect 7q11.23 dosage-dependent and GTF2I-specific disease mechanisms. We observed dosage-dependent impaired dynamics of neural progenitor proliferation, transcriptional imbalances, and highly specific alterations in neuronal output, leading to precocious excitatory neuron production in 7Dup, which was rescued by restoring physiological GTF2I levels. Transgenic mice with Gtf2i duplication recapitulated progenitor proliferation and neuronal differentiation defects alongside ASD-like behaviors. Consistently, inhibition of lysine demethylase 1 (LSD1), a GTF2I effector, was sufficient to rescue ASD-like phenotypes in transgenic mice, establishing GTF2I-LSD1 axis as a molecular pathway amenable to therapeutic intervention in ASD.

Resident memory T cell development is associated with AP-1 transcription factor upregulation across anatomical niches

Tissue-resident memory T (T RM ) cells play a central role in immune responses to pathogens across all barrier tissues after infection. However, the underlying mechanisms that drive T RM differentiation and priming for their recall effector function remains unclear. In this study, we leveraged both newly generated and publicly available single-cell RNA-sequencing (scRNAseq) data generated across 10 developmental time points to define features of CD8 T RM across both skin and small-intestine intraepithelial lymphocytes (siIEL). We employed linear modeling to capture temporally-associated gene programs that increase their expression levels in T cell subsets transitioning from an effector to a memory T cell state. In addition to capturing tissue-specific gene programs, we defined a consensus T RM signature of 60 genes across skin and siIEL that can effectively distinguish T RM from circulating T cell populations, providing a more specific T RM signature than what was previously generated by comparing bulk T RM to naïve or non-tissue resident memory populations. This updated T RM signature included the AP-1 transcription factor family members Fos, Fosb and Fosl2 . Moreover, ATACseq analysis detected an enrichment of AP-1-specific motifs at open chromatin sites in mature T RM . CyCIF tissue imaging detected nuclear co-localization of AP-1 members Fosb and Junb in resting CD8 T RM >100 days post-infection. Taken together, these results reveal a critical role of AP-1 transcription factor members in T RM biology and suggests a novel mechanism for rapid reactivation of resting T RM in tissue upon antigen encounter.

tidytof: a user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis

Summary

While many algorithms for analyzing high-dimensional cytometry data have now been developed, the software implementations of these algorithms remain highly customized-this means that exploring a dataset requires users to learn unique, often poorly interoperable package syntaxes for each step of data processing. To solve this problem, we developed {tidytof}, an open-source R package for analyzing high-dimensional cytometry data using the increasingly popular 'tidy data' interface.

Availability and implementation

{tidytof} is available at https://github.com/keyes-timothy/tidytof and is released under the MIT license. It is supported on Linux, MS Windows and MacOS. Additional documentation is available at the package website (https://keyes-timothy.github.io/tidytof/).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

A developmental atlas of somatosensory diversification and maturation in the dorsal root ganglia by single-cell mass cytometry

Precisely controlled development of the somatosensory system is essential for detecting pain, itch, temperature, mechanical touch and body position. To investigate the protein-level changes that occur during somatosensory development, we performed single-cell mass cytometry on dorsal root ganglia from C57/BL6 mice of both sexes, with litter replicates collected daily from embryonic day 11.5 to postnatal day 4. Measuring nearly 3 million cells, we quantified 30 molecularly distinct somatosensory glial and 41 distinct neuronal states across all timepoints. Analysis of differentiation trajectories revealed rare cells that co-express two or more Trk receptors and over-express stem cell markers, suggesting that these neurotrophic factor receptors play a role in cell fate specification. Comparison to previous RNA-based studies identified substantial differences between many protein-mRNA pairs, demonstrating the importance of protein-level measurements to identify functional cell states. Overall, this study demonstrates that mass cytometry is a high-throughput, scalable platform to rapidly phenotype somatosensory tissues.

Proper acquisition of cell class identity in organoids allows definition of fate specification programs of the human cerebral cortex

Realizing the full utility of brain organoids to study human development requires understanding whether organoids precisely replicate endogenous cellular and molecular events, particularly since acquisition of cell identity in organoids can be impaired by abnormal metabolic states. We present a comprehensive single-cell transcriptomic, epigenetic, and spatial atlas of human cortical organoid development, comprising over 610,000 cells, from generation of neural progenitors through production of differentiated neuronal and glial subtypes. We show that processes of cellular diversification correlate closely to endogenous ones, irrespective of metabolic state, empowering the use of this atlas to study human fate specification. We define longitudinal molecular trajectories of cortical cell types during organoid development, identify genes with predicted human-specific roles in lineage establishment, and uncover early transcriptional diversity of human callosal neurons. The findings validate this comprehensive atlas of human corticogenesis in vitro as a resource to prime investigation into the mechanisms of human cortical development.

NK cell receptor and ligand composition influences the clearance of SARS-CoV-2

To explore how the immune system controls clearance of SARS-CoV-2, we used a single-cell, mass cytometry-based proteomics platform to profile the immune systems of 21 patients who had recovered from SARS-CoV-2 infection without need for admission to an intensive care unit or for mechanical ventilation. We focused on receptors involved in interactions between immune cells and virus-infected cells. We found that the diversity of receptor repertoires on natural killer (NK) cells was negatively correlated with the viral clearance rate. In addition, NK subsets expressing the receptor DNAM1 were increased in patients who more rapidly recovered from infection. Ex vivo functional studies revealed that NK subpopulations with high DNAM1 expression had cytolytic activities in response to target cell stimulation. We also found that SARS-CoV-2 infection induced the expression of CD155 and nectin-4, ligands of DNAM1 and its paired coinhibitory receptor TIGIT, which counterbalanced the cytolytic activities of NK cells. Collectively, our results link the cytolytic immune responses of NK cells to the clearance of SARS-CoV-2 and show that the DNAM1 pathway modulates host-pathogen interactions during SARS-CoV-2 infection.

CD8+ TILs in NSCLC differentiate into TEMRA via a bifurcated trajectory: deciphering immunogenicity of tumor antigens

Background

CD8⁺ tumor-infiltrating lymphocytes (TILs) comprise phenotypically and functionally heterogeneous subpopulations. Of these, effector memory CD45RA re-expressing CD8⁺ T cells (Temra) have been discovered and characterized as the most terminally differentiated subset. However, their exact ontogeny and physiological importance in association with tumor progression remain poorly understood.

Methods

We analyzed primary tumors and peripheral blood samples from 26 patients with non-small cell lung cancer and analyzed their phenotypes and functional characteristics using flow cytometry, RNA-sequencing, and bioinformatics.

Results

We found that tumor-infiltrating Temra (tilTemra) cells largely differ from peripheral blood Temra (pTemra), with distinct transcriptomes and functional properties. Notably, although majority of the pTemra was CD27⁻CD28⁻ double-negative (DN), a large fraction of tilTemra population was CD27⁺CD28⁺ double-positive (DP), a characteristic of early-stage, less differentiated effector cells. Trajectory analysis revealed that CD8⁺ TILs undergo a divergent sequence of events for differentiation into either DP or DN tilTemra. Such a differentiation toward DP tilTemra relied on persistent expression of CD27 and CD28 and was associated with weak T cell receptor engagement. Thus, a higher proportion of DP Temra was correlated with lower immunogenicity of tumor antigens and consequently lower accumulation of CD8⁺ TILs.

Conclusions

These data suggest a complex interplay between CD8⁺ T cells and tumors and define DP Temra as a unique subset of tumor-specific CD8⁺ TILs that are produced in patients with relatively low immunogenic cancer types, predicting immunogenicity of tumor antigens and CD8⁺ TIL counts, a reliable biomarker for successful cancer immunotherapy.

Generalized and scalable trajectory inference in single-cell omics data with VIA

Inferring cellular trajectories using a variety of omic data is a critical task in single-cell data science. However, accurate prediction of cell fates, and thereby biologically meaningful discovery, is challenged by the sheer size of single-cell data, the diversity of omic data types, and the complexity of their topologies. We present VIA, a scalable trajectory inference algorithm that overcomes these limitations by using lazy-teleporting random walks to accurately reconstruct complex cellular trajectories beyond tree-like pathways (e.g., cyclic or disconnected structures). We show that VIA robustly and efficiently unravels the fine-grained sub-trajectories in a 1.3-million-cell transcriptomic mouse atlas without losing the global connectivity at such a high cell count. We further apply VIA to discovering elusive lineages and less populous cell fates missed by other methods across a variety of data types, including single-cell proteomic, epigenomic, multi-omics datasets, and a new in-house single-cell morphological dataset.

Machine Learning Approaches on High Throughput NGS Data to Unveil Mechanisms of Function in Biology and Disease

In this review, the fundamental basis of machine learning (ML) and data mining (DM) are summarized together with the techniques for distilling knowledge from state-of-the-art omics experiments. This includes an introduction to the basic mathematical principles of unsupervised/supervised learning methods, dimensionality reduction techniques, deep neural networks architectures and the applications of these in bioinformatics. Several case studies under evaluation mainly involve next generation sequencing (NGS) experiments, like deciphering gene expression from total and single cell (scRNA-seq) analysis; for the latter, a description of all recent artificial intelligence (AI) methods for the investigation of cell sub-types, biomarkers and imputation techniques are described. Other areas of interest where various ML schemes have been investigated are for providing information regarding transcription factors (TF) binding sites, chromatin organization patterns and RNA binding proteins (RBPs), while analyses on RNA sequence and structure as well as 3D dimensional protein structure predictions with the use of ML are described. Furthermore, we summarize the recent methods of using ML in clinical oncology, when taking into consideration the current omics data with pharmacogenomics to determine personalized treatments. With this review we wish to provide the scientific community with a thorough investigation of main novel ML applications which take into consideration the latest achievements in genomics, thus, unraveling the fundamental mechanisms of biology towards the understanding and cure of diseases.

Single cell genomics and developmental biology: moving beyond the generation of cell type catalogues

Major developmental processes such as gastrulation and early embryogenesis rely on a complex network of cell-cell interactions, chromatin remodeling, and transcriptional regulators. This makes it challenging to study early development when using bulk populations of cells. Recent advances in single-cell technologies have allowed researchers to better understand the interactions between different molecular modalities and the heterogeneities within classically defined cell types. As new single-cell technologies mature, they have the potential of providing a step-change in our understanding of embryogenesis. In this review, we summarize recent advances in single-cell technologies with particular focus on those that lend insight into early organogenesis. We then discuss current pitfalls and implications for future research.