Multiplexed single-cell characterization of alternative polyadenylation regulators.

Most mammalian genes have multiple polyA sites, representing a substantial source of transcript diversity regulated by the cleavage and polyadenylation (CPA) machinery. To better understand how these proteins govern polyA site choice, we introduce CPA-Perturb-seq, a multiplexed perturbation screen dataset of 42 CPA regulators with a 3' scRNA-seq readout that enables transcriptome-wide inference of polyA site usage. We develop a framework to detect perturbation-dependent changes in polyadenylation and characterize modules of co-regulated polyA sites. We find groups of intronic polyA sites regulated by distinct components of the nuclear RNA life cycle, including elongation, splicing, termination, and surveillance. We train and validate a deep neural network (APARENT-Perturb) for tandem polyA site usage, delineating a cis-regulatory code that predicts perturbation response and reveals interactions between regulatory complexes. Our work highlights the potential for multiplexed single-cell perturbation screens to further our understanding of post-transcriptional regulation.

An atlas of healthy and injured cell states and niches in the human kidney.

Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.

Consuming Mushrooms When Adopting a Healthy Mediterranean-Style Dietary Pattern Does Not Influence Short-Term Changes of Most Cardiometabolic Disease Risk Factors in Healthy Middle-Aged and Older Adults.

BACKGROUND: Mushrooms are a nutritious food, though knowledge of the effects of mushroom consumption on cardiometabolic risk factors is limited and inconsistent. OBJECTIVE: We assessed the effects of consuming mushrooms as part of a healthy United States Mediterranean-style dietary pattern (MED) on traditional and emerging cardiometabolic disease (CMD) risk factors. We hypothesized that adopting a MED diet with mushrooms would lead to greater improvements in multiple CMD risk factors. METHODS: Using a randomized, parallel study design, 60 adults (36 females, 24 males; aged 46 ± 12 y; body mass index 28.3 ± 2.84 kg/m2, mean ± standard deviation) without diagnosed CMD morbidities consumed a MED diet (all foods provided) without (control with breadcrumbs) or with 84 g/d of Agaricus bisporus (White Button, 4 d/wk) and Pleurotus ostreatus (Oyster, 3 d/wk) mushrooms for 8 wk. Fasting baseline and postintervention outcome measurements were traditional CMD risk factors, including blood pressure and fasting serum lipids, lipoproteins, glucose, and insulin. Exploratory CMD-related outcomes included lipoprotein particle sizes and indexes of inflammation. RESULTS: Adopting the MED-mushroom diet compared with the MED-control diet without mushrooms improved fasting serum glucose (change from baseline -2.9 ± 1.18 compared with 0.6 ± 1.10 mg/dL; time × group P = 0.034). Adopting the MED diet, independent of mushroom consumption, reduced serum total cholesterol (-10.2 ± 3.77 mg/dL; time P = 0.0001). Concomitantly, there was a reduction in high-density lipoprotein (HDL) cholesterol, buoyant HDL2b, and apolipoprotein A1, and an increase in lipoprotein(a) concentrations (main effect of time P < 0.05 for all). There were no changes in other measured CMD risk factors. CONCLUSIONS: Consuming a Mediterranean-style healthy dietary pattern with 1 serving/d of whole Agaricus bisporus and Pleurotus ostreatus mushrooms improved fasting serum glucose but did not influence other established or emerging CMD risk factors among middle-aged and older adults classified as overweight or obese but with clinically normal cardiometabolic health. TRIAL REGISTRATION NUMBER: https://www. CLINICALTRIALS: gov/study/NCT04259229?term=NCT04259229&rank=1.

Comparative analysis of multiplexed in situ gene expression profiling technologies.

The burgeoning interest in in situ multiplexed gene expression profiling technologies has opened new avenues for understanding cellular behavior and interactions. In this study, we present a comparative benchmark analysis of six in situ gene expression profiling methods, including both commercially available and academically developed methods, using publicly accessible mouse brain datasets. We find that standard sensitivity metrics, such as the number of unique molecules detected per cell, are not directly comparable across datasets due to substantial differences in the incidence of off-target molecular artifacts impacting specificity. To address these challenges, we explored various potential sources of molecular artifacts, developed novel metrics to control for them, and utilized these metrics to evaluate and compare different in situ technologies. Finally, we demonstrate how molecular false positives can seriously confound spatially-aware differential expression analysis, requiring caution in the interpretation of downstream results. Our analysis provides guidance for the selection, processing, and interpretation of in situ spatial technologies.

A Reproducibility Focused Meta-Analysis Method for Single-Cell Transcriptomic Case-Control Studies Uncovers Robust Differentially Expressed Genes.

Here we systematically studied the reproducibility of DEGs in previously published Alzheimer's Disease (AD), Parkinson's Disease (PD), and COVID-19 scRNA-seq studies. We found that while transcriptional scores created from differentially expressed genes (DEGs) in individual PD and COVID-19 datasets had moderate predictive power for the case control status of other datasets (mean AUC=0.77 and 0.75, respectively), genes from individual AD datasets had poor predictive power (mean AUC=0.68). We developed a non-parametric meta-analysis method, SumRank, based on reproducibility of relative differential expression ranks across datasets. The meta-analysis genes had improved predictive power (AUCs of 0.88, 0.91, and 0.78, respectively). By multiple other metrics, specificity and sensitivity of these genes were substantially higher than those discovered by dataset merging and inverse variance weighted p-value aggregation methods. The DEGs revealed known and novel biological pathways, and we validate the BCAT1 gene as down-regulated in oligodendrocytes in an AD mouse model. Our analyses show that for heterogeneous diseases, DEGs of individual studies often have low reproducibility, but combining information across multiple datasets promotes the rigorous discovery of reproducible DEGs.

Dictionary learning for integrative, multimodal and scalable single-cell analysis.

Mapping single-cell sequencing profiles to comprehensive reference datasets provides a powerful alternative to unsupervised analysis. However, most reference datasets are constructed from single-cell RNA-sequencing data and cannot be used to annotate datasets that do not measure gene expression. Here we introduce 'bridge integration', a method to integrate single-cell datasets across modalities using a multiomic dataset as a molecular bridge. Each cell in the multiomic dataset constitutes an element in a 'dictionary', which is used to reconstruct unimodal datasets and transform them into a shared space. Our procedure accurately integrates transcriptomic data with independent single-cell measurements of chromatin accessibility, histone modifications, DNA methylation and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to improve computational scalability and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach, implemented in version 5 of our Seurat toolkit ( http://www.satijalab.org/seurat ), broadens the utility of single-cell reference datasets and facilitates comparisons across diverse molecular modalities.

Phospho-seq: Integrated, multi-modal profiling of intracellular protein dynamics in single cells.

Cell signaling plays a critical role in regulating cellular behavior and fate. While multimodal single-cell sequencing technologies are rapidly advancing, scalable and flexible profiling of cell signaling states alongside other molecular modalities remains challenging. Here we present Phospho-seq, an integrated approach that aims to quantify phosphorylated intracellular and intranuclear proteins, and to connect their activity with cis-regulatory elements and transcriptional targets. We utilize a simplified benchtop antibody conjugation method to create large custom antibody panels for simultaneous protein and scATAC-seq profiling on whole cells, and integrate this information with scRNA-seq datasets via bridge integration. We apply our workflow to cell lines, induced pluripotent stem cells, and 3-month-old brain organoids to demonstrate its broad applicability. We demonstrate that Phospho-seq can define cellular states and trajectories, reconstruct gene regulatory relationships, and characterize the causes and consequences of heterogeneous cell signaling in neurodevelopment.

CPA-Perturb-seq: Multiplexed single-cell characterization of alternative polyadenylation regulators.

Most mammalian genes have multiple polyA sites, representing a substantial source of transcript diversity that is governed by the cleavage and polyadenylation (CPA) regulatory machinery. To better understand how these proteins govern polyA site choice we introduce CPA-Perturb-seq, a multiplexed perturbation screen dataset of 42 known CPA regulators with a 3' scRNA-seq readout that enables transcriptome-wide inference of polyA site usage. We develop a statistical framework to specifically identify perturbation-dependent changes in intronic and tandem polyadenylation, and discover modules of co-regulated polyA sites exhibiting distinct functional properties. By training a multi-task deep neural network (APARENT-Perturb) on our dataset, we delineate a cis-regulatory code that predicts responsiveness to perturbation and reveals interactions between distinct regulatory complexes. Finally, we leverage our framework to re-analyze published scRNA-seq datasets, identifying new regulators that affect the relative abundance of alternatively polyadenylated transcripts, and characterizing extensive cellular heterogeneity in 3' UTR length amongst antibody-producing cells. Our work highlights the potential for multiplexed single-cell perturbation screens to further our understanding of post-transcriptional regulation in vitro and in vivo.

Effects of Consuming Ounce-Equivalent Portions of Animal- vs. Plant-Based Protein Foods, as Defined by the Dietary Guidelines for Americans on Essential Amino Acids Bioavailability in Young and Older Adults: Two Cross-Over Randomized Controlled Trials.

BACKGROUND: The Dietary Guidelines for Americans (DGA) recommends consuming a variety of "Protein Foods" based on "ounce-equivalent" (oz-eq) portions. No study has assessed the same oz-eq portions of animal- vs. plant-based protein foods on essential amino acid (EAA) bioavailability for protein anabolism in young and older adults. OBJECTIVES: We assessed the effects of consuming two oz-eq portions of pork, eggs, black beans, and almonds on postprandial EAA bioavailability in young and older adults. METHODS: We conducted two investigator-blinded, randomized crossover trials in young (n = 30; mean age ± SD: 26.0 ± 4.9 y) and older adults (n = 25; mean age ± SD: 64.2 ± 6.6 y). Participants completed four testing sessions where they consumed a standardized meal with two oz-eq of either unprocessed lean pork, whole eggs, black beans, or sliced almonds. Blood samples were taken at baseline and 30, 60, 120, 180, 240, and 300 min postprandially. Plasma EAA bioavailability was based on postprandial integrated positive areas under the curve. RESULTS: Participant age did not affect EAA bioavailability among the four protein foods tested. Two oz-eq portions of pork (7.36 g EAA) and eggs (5.38 g EAA) resulted in greater EAA bioavailability than black beans (3.02 g EAA) and almonds (1.85 g EAA) in young and older adults, separately or combined (p < 0.0001 for all). Pork resulted in greater EAA bioavailability than eggs in young adults (p < 0.0001), older adults (p = 0.0007), and combined (p < 0.0001). There were no differences in EAA bioavailability between black beans and almonds. CONCLUSIONS: The same "oz-eq" portions of animal- and plant-based protein foods do not provide equivalent EAA content and postprandial bioavailability for protein anabolism in young and older adults.

Complete Genome Sequences of Diverse Uropathogenic Staphylococcus saprophyticus Isolates from a College Health Center.

Staphylococcus saprophyticus is a significant cause of urinary tract infections in younger women, but it has been understudied at the genomic level. We report genome sequences of six S. saprophyticus isolates obtained from female patients who presented with urinary tract infection symptoms at a college health center in 2019.

F Plasmids Are the Major Carriers of Antibiotic Resistance Genes in Human-Associated Commensal Escherichia coli.

The evolution and propagation of antibiotic resistance by bacterial pathogens are significant threats to global public health. Contemporary DNA sequencing tools were applied here to gain insight into carriage of antibiotic resistance genes in Escherichia coli, a ubiquitous commensal bacterium in the gut microbiome in humans and many animals, and a common pathogen. Draft genome sequences generated for a collection of 101 E. coli strains isolated from healthy undergraduate students showed that horizontally acquired antibiotic resistance genes accounted for most resistance phenotypes, the primary exception being resistance to quinolones due to chromosomal mutations. A subset of 29 diverse isolates carrying acquired resistance genes and 21 control isolates lacking such genes were further subjected to long-read DNA sequencing to enable complete or nearly complete genome assembly. Acquired resistance genes primarily resided on F plasmids (101/153 [67%]), with smaller numbers on chromosomes (30/153 [20%]), IncI complex plasmids (15/153 [10%]), and small mobilizable plasmids (5/153 [3%]). Nearly all resistance genes were found in the context of known transposable elements. Very few structurally conserved plasmids with antibiotic resistance genes were identified, with the exception of an ∼90-kb F plasmid in sequence type 1193 (ST1193) isolates that appears to serve as a platform for resistance genes and may have virulence-related functions as well. Carriage of antibiotic resistance genes on transposable elements and mobile plasmids in commensal E. coli renders the resistome highly dynamic.IMPORTANCE Rising antibiotic resistance in human-associated bacterial pathogens is a serious threat to our ability to treat many infectious diseases. It is critical to understand how acquired resistance genes move in and through bacteria associated with humans, particularly for species such as Escherichia coli that are very common in the human gut but can also be dangerous pathogens. This work combined two distinct DNA sequencing approaches to allow us to explore the genomes of E. coli from college students to show that the antibiotic resistance genes these bacteria have acquired are usually carried on a specific type of plasmid that is naturally transferrable to other E. coli, and likely to other related bacteria.