Engineering highly active nuclease enzymes with machine learning and high-throughput screening.

Optimizing enzymes to function in novel chemical environments is a central goal of synthetic biology, but optimization is often hindered by a rugged fitness landscape and costly experiments. In this work, we present TeleProt, a machine learning (ML) framework that blends evolutionary and experimental data to design diverse protein libraries, and employ it to improve the catalytic activity of a nuclease enzyme that degrades biofilms that accumulate on chronic wounds. After multiple rounds of high-throughput experiments, TeleProt found a significantly better top-performing enzyme than directed evolution (DE), had a better hit rate at finding diverse, high-activity variants, and was even able to design a high-performance initial library using no prior experimental data.

Consensus prediction of cell type labels in single-cell data with popV.

Cell-type classification is a crucial step in single-cell sequencing analysis. Various methods have been proposed for transferring a cell-type label from an annotated reference atlas to unannotated query datasets. Existing methods for transferring cell-type labels lack proper uncertainty estimation for the resulting annotations, limiting interpretability and usefulness.

Rv0928 protein facilitates macrophage control of mycobacterium infection by promoting mitochondrial intrinsic apoptosis and ROS-mediated inflammation.

Macrophages are the main target cells for (Mtb) infection. Previous studies have shown that Mtb actively upregulates phosphorus transport proteins, such as Rv0928 protein (also known as PstS3), to increase inorganic phosphate uptake and promote their survival under low phosphorus culture conditions . However, it is unclear whether this upregulation of PstS3 affects the intracellular survival of Mtb, as the latter is also largely dependent on the immune response of infected macrophages.

Anti-GD2 CAR-NKT cells in relapsed or refractory neuroblastoma: updated phase 1 trial interim results.

Vα24-invariant natural killer T cells (NKTs) have anti-tumor properties that can be enhanced by chimeric antigen receptors (CARs). Here we report updated interim results from the first-in-human phase 1 evaluation of autologous NKTs co-expressing a GD2-specific CAR with interleukin 15 (IL15) (GD2-CAR.15) in 12 children with neuroblastoma (NB).

Indoleamine 2,3-dioxygenase 1 activation in mature cDC1 promotes tolerogenic education of inflammatory cDC2 via metabolic communication.

Conventional dendritic cells (cDCs), cDC1 and cDC2, act both to initiate immunity and maintain self-tolerance. The tryptophan metabolic enzyme indoleamine 2,3-dioxygenase 1 (IDO1) is used by cDCs in maintaining tolerance, but its role in different subsets remains unclear. At homeostasis, only mature CCR7 cDC1 expressed IDO1 that was dependent on IRF8.

The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans.

Molecular characterization of cell types using single-cell transcriptome sequencing is revolutionizing cell biology and enabling new insights into the physiology of human organs. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues, and tissue-specific variation in gene expression.

Mucosal Vaccination with Cyclic Dinucleotide Adjuvants Induces Effective T Cell Homing and IL-17-Dependent Protection against Infection.

Tuberculosis consistently causes more deaths worldwide annually than any other single pathogen, making new effective vaccines an urgent priority for global public health. Among potential adjuvants, STING-activating cyclic dinucleotides (CDNs) uniquely stimulate a cytosolic sensing pathway activated only by pathogens. Recently, we demonstrated that a CDN-adjuvanted protein subunit vaccine robustly protects against tuberculosis infection in mice.

Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models.

As the number of single-cell transcriptomics datasets grows, the natural next step is to integrate the accumulating data to achieve a common ontology of cell types and states. However, it is not straightforward to compare gene expression levels across datasets and to automatically assign cell type labels in a new dataset based on existing annotations. In this manuscript, we demonstrate that our previously developed method, scVI, provides an effective and fully probabilistic approach for joint representation and analysis of scRNA-seq data, while accounting for uncertainty caused by biological and measurement noise.

Inference of single-cell phylogenies from lineage tracing data using Cassiopeia.

The pairing of CRISPR/Cas9-based gene editing with massively parallel single-cell readouts now enables large-scale lineage tracing. However, the rapid growth in complexity of data from these assays has outpaced our ability to accurately infer phylogenetic relationships. First, we introduce Cassiopeia-a suite of scalable maximum parsimony approaches for tree reconstruction.

Integrated single cell analysis of blood and cerebrospinal fluid leukocytes in multiple sclerosis.

Cerebrospinal fluid (CSF) protects the central nervous system (CNS) and analyzing CSF aids the diagnosis of CNS diseases, but our understanding of CSF leukocytes remains superficial. Here, using single cell transcriptomics, we identify a specific location-associated composition and transcriptome of CSF leukocytes. Multiple sclerosis (MS) - an autoimmune disease of the CNS - increases transcriptional diversity in blood, but increases cell type diversity in CSF including a higher abundance of cytotoxic phenotype T helper cells.

Simulating multiple faceted variability in single cell RNA sequencing.

The abundance of new computational methods for processing and interpreting transcriptomes at a single cell level raises the need for in silico platforms for evaluation and validation. Here, we present SymSim, a simulator that explicitly models the processes that give rise to data observed in single cell RNA-Seq experiments. The components of the SymSim pipeline pertain to the three primary sources of variation in single cell RNA-Seq data: noise intrinsic to the process of transcription, extrinsic variation indicative of different cell states (both discrete and continuous), and technical variation due to low sensitivity and measurement noise and bias.

Natural selection interacts with recombination to shape the evolution of hybrid genomes.

To investigate the consequences of hybridization between species, we studied three replicate hybrid populations that formed naturally between two swordtail fish species, estimating their fine-scale genetic map and inferring ancestry along the genomes of 690 individuals. In all three populations, ancestry from the "minor" parental species is more common in regions of high recombination and where there is linkage to fewer putative targets of selection. The same patterns are apparent in a reanalysis of human and archaic admixture.

What are the predictors of major depression in adult patients with epilepsy?

Epilepsy is not only a chronic neurological disorder but also a condition associated with other comorbidities. The purpose of this study was to determine the prevalence of major depression in a cohort of adult patients with epilepsy (PWEs) living in north China, and investigate the predictors of major depression in PWEs. A total of 215 consecutive cases were enrolled and divided into two groups: PWEs with major depression and PWEs without major depression.