Revealing the grammar of small RNA secretion using interpretable machine learning.

Small non-coding RNAs can be secreted through a variety of mechanisms, including exosomal sorting, in small extracellular vesicles, and within lipoprotein complexes. However, the mechanisms that govern their sorting and secretion are not well understood. Here, we present ExoGRU, a machine learning model that predicts small RNA secretion probabilities from primary RNA sequences. We experimentally validated the performance of this model through ExoGRU-guided mutagenesis and synthetic RNA sequence analysis. Additionally, we used ExoGRU to reveal cis and trans factors that underlie small RNA secretion, including known and novel RNA-binding proteins (RBPs), e.g., YBX1, HNRNPA2B1, and RBM24. We also developed a novel technique called exoCLIP, which reveals the RNA interactome of RBPs within the cell-free space. Together, our results demonstrate the power of machine learning in revealing novel biological mechanisms. In addition to providing deeper insight into small RNA secretion, this knowledge can be leveraged in therapeutic and synthetic biology applications.

A rat liver cell atlas reveals intrahepatic myeloid heterogeneity.

The large size and vascular accessibility of the laboratory rat (Rattus norvegicus) make it an ideal hepatic animal model for diseases that require surgical manipulation. Often, the disease susceptibility and outcomes of inflammatory pathologies vary significantly between strains. This study uses single-cell transcriptomics to better understand the complex cellular network of the rat liver, as well as to unravel the cellular and molecular sources of inter-strain hepatic variation. We generated single-cell and single-nucleus transcriptomic maps of the livers of healthy Dark Agouti and Lewis rat strains and developed a factor analysis-based bioinformatics analysis pipeline to study data covariates, such as strain and batch. Using this approach, we discovered transcriptomic variation within the hepatocyte and myeloid populations that underlie distinct cell states between rat strains. This finding will help provide a reference for future investigations on strain-dependent outcomes of surgical experiment models.

Genotyping SARS-CoV-2 Variants Using Ratiometric Nucleic Acid Barcode Panels.

Designing diagnostic assays to genotype rapidly mutating viruses remains a challenge despite the overall improvements in nucleic acid detection technologies. RT-PCR and next-generation sequencing are unsuitable for genotyping during outbreaks or in point-of-care detection due to their infrastructure requirements and longer turnaround times. We developed a quantum dot barcode multiplexing system to genotype mutated viruses. We designed multiple quantum dot barcodes to target conserved, wildtype, and mutated regions of SARS-CoV-2. We calculated ratios of the signal output from different barcodes that enabled SARS-CoV-2 detection and identified SARS-CoV-2 variant strains from a sample. We detected different sequence types, including conserved genes, nucleotide deletions, and single nucleotide substitutions. Our system detected SARS-CoV-2 patient specimens with 98% sensitivity and 94% specificity across 91 patient samples. Further, we leveraged our barcoding and ratio system to track the emergence of the N501Y SARS-CoV-2 mutation from December 2020 to May 2021 and demonstrated that the more transmissible N501Y mutation started to dominate infections by April 2021. Our barcoding and signal ratio approach can genotype viruses and track the emergence of viral mutations in a single diagnostic test. This technology can be extended to tracking other viruses. Combined with smartphone detection technologies, this assay can be adapted for point-of-care tracking of viral mutations in real time.

Single-cell profiling of healthy human kidney reveals features of sex-based transcriptional programs and tissue-specific immunity.

Knowledge of the transcriptional programs underpinning the functions of human kidney cell populations at homeostasis is limited. We present a single-cell perspective of healthy human kidney from 19 living donors, with equal contribution from males and females, profiling the transcriptome of 27677 cells to map human kidney at high resolution. Sex-based differences in gene expression within proximal tubular cells were observed, specifically, increased anti-oxidant metallothionein genes in females and aerobic metabolism-related genes in males. Functional differences in metabolism were confirmed in proximal tubular cells, with male cells exhibiting higher oxidative phosphorylation and higher levels of energy precursor metabolites. We identified kidney-specific lymphocyte populations with unique transcriptional profiles indicative of kidney-adapted functions. Significant heterogeneity in myeloid cells was observed, with a MRC1+LYVE1+FOLR2+C1QC+ population representing a predominant population in healthy kidney. This study provides a detailed cellular map of healthy human kidney, and explores the complexity of parenchymal and kidney-resident immune cells.

Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods.

Single-cell transcriptomics can profile thousands of cells in a single experiment and identify novel cell types, states and dynamics in a wide variety of tissues and organisms. Standard experimental protocols and analysis workflows have been developed to create single-cell transcriptomic maps from tissues. This tutorial focuses on how to interpret these data to identify cell types, states and other biologically relevant patterns with the objective of creating an annotated map of cells. We recommend a three-step workflow including automatic cell annotation (wherever possible), manual cell annotation and verification. Frequently encountered challenges are discussed, as well as strategies to address them. Guiding principles and specific recommendations for software tools and resources that can be used for each step are covered, and an R notebook is included to help run the recommended workflow. Basic familiarity with computer software is assumed, and basic knowledge of programming (e.g., in the R language) is recommended.