UMAP reveals cryptic population structure and phenotype heterogeneity in large genomic cohorts

Transcriptional adaptation of olfactory sensory neurons to GPCR identity and activity

In mammals, chemoperception relies on a diverse set of neuronal sensors able to detect chemicals present in the environment, and to adapt to various levels of stimulation. The contribution of endogenous and external factors to these neuronal identities remains to be determined. Taking advantage of the parallel coding lines present in the olfactory system, we explored the potential variations of neuronal identities before and after olfactory experience. We found that at rest, the transcriptomic profiles of mouse olfactory sensory neuron populations are already divergent, specific to the olfactory receptor they express, and are associated with the sequence of these latter. These divergent profiles further evolve in response to the environment, as odorant exposure leads to reprogramming via the modulation of transcription. These findings highlight a broad range of sensory neuron identities that are present at rest and that adapt to the experience of the individual, thus adding to the complexity and flexibility of sensory coding.

Disrupting mechanotransduction decreases fibrosis and contracture in split-thickness skin grafting

Burns and other traumatic injuries represent a substantial biomedical burden. The current standard of care for deep injuries is autologous split-thickness skin grafting (STSG), which frequently results in contractures, abnormal pigmentation, and loss of biomechanical function. Currently, there are no effective therapies that can prevent fibrosis and contracture after STSG. Here, we have developed a clinically relevant porcine model of STSG and comprehensively characterized porcine cell populations involved in healing with single-cell resolution. We identified an up-regulation of proinflammatory and mechanotransduction signaling pathways in standard STSGs. Blocking mechanotransduction with a small-molecule focal adhesion kinase (FAK) inhibitor promoted healing, reduced contracture, mitigated scar formation, restored collagen architecture, and ultimately improved graft biomechanical properties. Acute mechanotransduction blockade up-regulated myeloid CXCL10-mediated anti-inflammation with decreased CXCL14-mediated myeloid and fibroblast recruitment. At later time points, mechanical signaling shifted fibroblasts toward profibrotic differentiation fates, and disruption of mechanotransduction modulated mesenchymal fibroblast differentiation states to block those responses, instead driving fibroblasts toward proregenerative, adipogenic states similar to unwounded skin. We then confirmed these two diverging fibroblast transcriptional trajectories in human skin, human scar, and a three-dimensional organotypic model of human skin. Together, pharmacological blockade of mechanotransduction markedly improved large animal healing after STSG by promoting both early, anti-inflammatory and late, regenerative transcriptional programs, resulting in healed tissue similar to unwounded skin. FAK inhibition could therefore supplement the current standard of care for traumatic and burn injuries.

Genome-wide analyses of introgression between two sympatric Asian oak species

Introgression can be an important source of new alleles for adaption under rapidly changing environments, perhaps even more important than standing variation. Though introgression has been extensively studied in many plants and animals, key questions on the underlying mechanisms of introgression still remain unanswered. In particular, we are yet to determine the genomic distribution of introgressed regions along the genome; whether the extent and patterns of introgression are influenced by ecological factors; and when and how introgression contributes to adaptation. Here, we generated high-quality genomic resources for two sympatric widespread Asian oak species, Quercus acutissima and Q. variabilis, sampled in multiple forests to study introgression between them. We show that introgressed regions are broadly distributed across the genome. Introgression was affected by genetic divergence between pairs of populations and by the similarity of the environments in which they live-populations occupying similar ecological sites tended to share the same introgressed regions. Introgressed genomic footprints of adaptation were preferentially located in regions with suppressed recombination rate. Introgression probably confers adaptation in these oak populations by introducing allelic variation in cis-regulatory elements, in particular through transposable element insertions, thereby altering the regulation of genes related to stress. Our results provide new avenues of research for uncovering mechanisms of adaptation due to hybridization in sympatric species.

High-Dimensional Fluctuations in Liquid Water: Combining Chemical Intuition with Unsupervised Learning

The microscopic description of the local structure of water remains an open challenge. Here, we adopt an agnostic approach to understanding water's hydrogen bond network using data harvested from molecular dynamics simulations of an empirical water model. A battery of state-of-the-art unsupervised data-science techniques are used to characterize the free-energy landscape of water starting from encoding the water environment using local atomic descriptors, through dimensionality reduction and finally the use of advanced clustering techniques. Analysis of the free energy under ambient conditions was found to be consistent with a rough single basin and independent of the choice of the water model. We find that the fluctuations of the water network occur in a high-dimensional space, which we characterize using a combination of both atomic descriptors and chemical-intuition-based coordinates. We demonstrate that a combination of both types of variables is needed in order to adequately capture the complexity of the fluctuations in the hydrogen bond network at different length scales both at room temperature and also close to the critical point of water. Our results provide a general framework for examining fluctuations in water under different conditions.

Inferring population structure in biobank-scale genomic data

Inferring the structure of human populations from genetic variation data is a key task in population and medical genomic studies. Although a number of methods for population structure inference have been proposed, current methods are impractical to run on biobank-scale genomic datasets containing millions of individuals and genetic variants. We introduce SCOPE, a method for population structure inference that is orders of magnitude faster than existing methods while achieving comparable accuracy. SCOPE infers population structure in about a day on a dataset containing one million individuals and variants as well as on the UK Biobank dataset containing 488,363 individuals and 569,346 variants. Furthermore, SCOPE can leverage allele frequencies from previous studies to improve the interpretability of population structure estimates.

The application of Uniform Manifold Approximation and Projection (UMAP) for unconstrained ordination and classification of biological indicators in aquatic ecology

The analysis of community structure in studies of freshwater ecology often requires the application of dimensionality reduction to process multivariate data. A high number of dimensions (number of taxa/environmental parameters × number of samples), nonlinear relationships, outliers, and high variability usually hinder the visualization and interpretation of multivariate datasets. Here, we proposed a new statistical design using Uniform Manifold Approximation and Projection (UMAP), and community partitioning using Louvain algorithms, to ordinate and classify the structure of aquatic biota in two-dimensional space. We present this approach with a demonstration of five previously published datasets for diatoms, macrophytes, chironomids (larval and subfossil), and fish. Principal Component Analysis (PCA) and Ward's clustering were also used to assess the comparability of the UMAP approach compared to traditional approaches for ordination and classification. The ordination of sampling sites in 2-dimensional space showed a much denser, and easier to interpret, grouping using the UMAP approach in comparison to PCA. The classification of community structure using the Louvain algorithm in UMAP ordinal space showed a high classification strength for data with a high number of dimensions than the cluster patterns obtained with the use of a Ward's algorithm in PCA. Environmental gradients, presented via heat maps, were overlayed with the ordination patterns of aquatic communities, confirming that the ordinations obtained by UMAP were ecologically meaningful. This is the first study that has applied a UMAP approach with classification using Louvain algorithms on ecological datasets. We show that the performance of local and global structures, as well as the number of clusters determined by the algorithm, make this approach more powerful than traditional approaches.

Behavior of Linear and Nonlinear Dimensionality Reduction for Collective Variable Identification of Small Molecule Solution-Phase Reactions

Identifying collective variables (CVs) for chemical reactions is essential to reduce the 3N-dimensional energy landscape into lower dimensional basins and barriers of interest. However, in condensed phase processes, the nonmeaningful motions of bulk solvent often overpower the ability of dimensionality reduction methods to identify correlated motions that underpin collective variables. Yet solvent can play important indirect or direct roles in reactivity, and much can be lost through treatments that remove or dampen solvent motion. This has been amply demonstrated within principal component analysis (PCA), although less is known about the behavior of nonlinear dimensionality reduction methods, e.g., uniform manifold approximation and projection (UMAP), that have become recently utilized. The latter presents an interesting alternative to linear methods though often at the expense of interpretability. This work presents distance-attenuated projection methods of atomic coordinates that facilitate the application of both PCA and UMAP to identify collective variables in the presence of explicit solvent and further the specific identity of solvent molecules that participate in chemical reactions. The performance of both methods is examined in detail for two reactions where the explicit solvent plays very different roles within the collective variables. When applied to raw molecular dynamics data in solution, both PCA and UMAP representations are dominated by bulk solvent motions. On the other hand, when applied to data preprocessed by our attenuated projection methods, both PCA and UMAP identify the appropriate collective variables (though varying sensitivity is observed due to the presence of explicit solvent that results from the projection method). Importantly, this approach allows identification of specific solvent molecules that are relevant to the CVs and their importance.

A framework for research into continental ancestry groups of the UK Biobank

BACKGROUND

The UK Biobank is a large prospective cohort, based in the UK, that has deep phenotypic and genomic data on roughly a half a million individuals. Included in this resource are data on approximately 78,000 individuals with "non-white British ancestry." While most epidemiology studies have focused predominantly on populations of European ancestry, there is an opportunity to contribute to the study of health and disease for a broader segment of the population by making use of the UK Biobank's "non-white British ancestry" samples. Here, we present an empirical description of the continental ancestry and population structure among the individuals in this UK Biobank subset.

RESULTS

Reference populations from the 1000 Genomes Project for Africa, Europe, East Asia, and South Asia were used to estimate ancestry for each individual. Those with at least 80% ancestry in one of these four continental ancestry groups were taken forward (N = 62,484). Principal component and K-means clustering analyses were used to identify and characterize population structure within each ancestry group. Of the approximately 78,000 individuals in the UK Biobank that are of "non-white British" ancestry, 50,685, 6653, 2782, and 2364 individuals were associated to the European, African, South Asian, and East Asian continental ancestry groups, respectively. Each continental ancestry group exhibits prominent population structure that is consistent with self-reported country of birth data and geography.

CONCLUSIONS

Methods outlined here provide an avenue to leverage UK Biobank's deeply phenotyped data allowing researchers to maximize its potential in the study of health and disease in individuals of non-white British ancestry.

Fast, low-memory detection and localization of large, polymorphic inversions from SNPs

BACKGROUND

Large (>1 Mb), polymorphic inversions have substantial impacts on population structure and maintenance of genotypes. These large inversions can be detected from single nucleotide polymorphism (SNP) data using unsupervised learning techniques like PCA. Construction and analysis of a feature matrix from millions of SNPs requires large amount of memory and limits the sizes of data sets that can be analyzed.

METHODS

We propose using feature hashing construct a feature matrix from a VCF file of SNPs for reducing memory usage. The matrix is constructed in a streaming fashion such that the entire VCF file is never loaded into memory at one time.

RESULTS

When evaluated on Anopheles mosquito and Drosophila fly data sets, our approach reduced memory usage by 97% with minimal reductions in accuracy for inversion detection and localization tasks.

CONCLUSION

With these changes, inversions in larger data sets can be analyzed easily and efficiently on common laptop and desktop computers. Our method is publicly available through our open-source inversion analysis software, Asaph.

Using bi-dimensional representations to understand patterns in COVID-19 blood exam data

Blood tests play an essential role in everyday medicine and are used by doctors in several diagnostic procedures. Moreover, this data is multivariate - and often some diseases, such as COVID-19, could have different symptom manifestations and outcomes. This study proposes a method of extracting useful information from blood tests using UMAP technique - Uniform Manifold Approximation and Projection for Dimension Reduction combined with DBSCAN clustering and statistical approaches. The analysis performed here indicates several clusters of infection prevalence varying between 2%-37%, showing that our procedure is indeed capable of finding different patterns. A possible explanation is that COVID-19 is not just a respiratory infection but a systemic disease with critical hematological implications, primarily on white-cell fractions, as indicated by relevant statistical test p -values in the range of 0.03-0.1. The novel analysis procedure proposed could be adopted in other data-sets of different illnesses to help researchers to discover new patterns of data that could be used in various diseases and contexts.

Is it left or is it right? A classification approach for investigating hemispheric differences in low and high dimensionality

Hemispheric asymmetries, i.e., differences between the two halves of the brain, have extensively been studied with respect to both structure and function. Commonly employed pairwise comparisons between left and right are suitable for finding differences between the hemispheres, but they come with several caveats when assessing multiple asymmetries. What is more, they are not designed for identifying the characterizing features of each hemisphere. Here, we present a novel data-driven framework—based on machine learning-based classification—for identifying the characterizing features that underlie hemispheric differences. Using voxel-based morphometry data from two different samples (n = 226, n = 216), we separated the hemispheres along the midline and used two different pipelines: First, for investigating global differences, we embedded the hemispheres into a two-dimensional space and applied a classifier to assess if the hemispheres are distinguishable in their low-dimensional representation. Second, to investigate which voxels show systematic hemispheric differences, we employed two classification approaches promoting feature selection in high dimensions. The two hemispheres were accurately classifiable in both their low-dimensional (accuracies: dataset 1 = 0.838; dataset 2 = 0.850) and high-dimensional (accuracies: dataset 1 = 0.966; dataset 2 = 0.959) representations. In low dimensions, classification of the right hemisphere showed higher precision (dataset 1 = 0.862; dataset 2 = 0.894) compared to the left hemisphere (dataset 1 = 0.818; dataset 2 = 0.816). A feature selection algorithm in the high-dimensional analysis identified voxels that most contribute to accurate classification. In addition, the map of contributing voxels showed a better overlap with moderate to highly lateralized voxels, whereas conventional t test with threshold-free cluster enhancement best resembled the LQ map at lower thresholds. Both the low- and high-dimensional classifiers were capable of identifying the hemispheres in subsamples of the datasets, such as males, females, right-handed, or non-right-handed participants. Our study indicates that hemisphere classification is capable of identifying the hemisphere in their low- and high-dimensional representation as well as delineating brain asymmetries. The concept of hemisphere classifiability thus allows a change in perspective, from asking what differs between the hemispheres towards focusing on the features needed to identify the left and right hemispheres. Taking this perspective on hemispheric differences may contribute to our understanding of what makes each hemisphere special.

Xenogeneic skin transplantation promotes angiogenesis and tissue regeneration through activated Trem2+ macrophages

Skin allo- and xenotransplantation are the standard treatment for major burns when donor sites for autografts are not available. The relationship between the immune response to foreign grafts and their impact on wound healing has not been fully elucidated. Here, we investigated changes in collagen architecture after xenogeneic implantation of human biologic scaffolds. We show that collagen deposition in response to the implantation of human split-thickness skin grafts (hSTSGs) containing live cells recapitulates normal skin architecture, whereas human acellular dermal matrix (ADM) grafts led to a fibrotic collagen deposition. We show that macrophage differentiation in response to hSTSG implantation is driven toward regenerative Trem2+ subpopulations and found that hydrogel delivery of these cells significantly accelerated wound closure. Our study identifies the preclinical therapeutic potential of Trem2+ macrophages to mitigate fibrosis and promote wound healing, providing a novel effective strategy to develop advanced cell therapies for complex wounds.

Why do we pick similar mates, or do we?

Humans often mate with those resembling themselves, a phenomenon described as positive assortative mating (PAM). The causes of this attract broad interest, but there is little agreement on the topic. This may be because empirical studies and reviews sometimes focus on just a few explanations, often based on disciplinary conventions. This review presents an interdisciplinary conceptual framework on the causes of PAM in humans, drawing on human and non-human biology, the social sciences, and the humanities. Viewing causality holistically, we first discuss the proximate causes (i.e. the 'how') of PAM, considering three mechanisms: stratification, convergence and mate choice. We also outline methods to control for confounders when studying mate choice. We then discuss ultimate explanations (i.e. 'the why') for PAM, including adaptive and non-adaptive processes. We conclude by suggesting a focus on interdisciplinarity in future research.

A population-specific reference panel for improved genotype imputation in African Americans

There is currently a dearth of accessible whole genome sequencing (WGS) data for individuals residing in the Americas with Sub-Saharan African ancestry. We generated whole genome sequencing data at intermediate (15×) coverage for 2,294 individuals with large amounts of Sub-Saharan African ancestry, predominantly Atlantic African admixed with varying amounts of European and American ancestry. We performed extensive comparisons of variant callers, phasing algorithms, and variant filtration on these data to construct a high quality imputation panel containing data from 2,269 unrelated individuals. With the exception of the TOPMed imputation server (which notably cannot be downloaded), our panel substantially outperformed other available panels when imputing African American individuals. The raw sequencing data, variant calls and imputation panel for this cohort are all freely available via dbGaP and should prove an invaluable resource for further study of admixed African genetics.

Using explainable machine learning to identify patients at risk of reattendance at discharge from emergency departments

Short-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance could help reduce the number of missed critical illnesses and could reduce avoidable utilization of emergency departments by enabling targeted post-discharge intervention. In this manuscript, we present a retrospective, single-centre study where we created and evaluated an extreme gradient boosting decision tree model trained to identify patients at risk of reattendance within 72 h of discharge from an emergency department (University Hospitals Southampton Foundation Trust, UK). Our model was trained using 35,447 attendances by 28,945 patients and evaluated on a hold-out test set featuring 8847 attendances by 7237 patients. The set of attendances from a given patient appeared exclusively in either the training or the test set. Our model was trained using both visit level variables (e.g., vital signs, arrival mode, and chief complaint) and a set of variables available in a patients electronic patient record, such as age and any recorded medical conditions. On the hold-out test set, our highest performing model obtained an AUROC of 0.747 (95% CI 0.722–0.773) and an average precision of 0.233 (95% CI 0.194–0.277). These results demonstrate that machine-learning models can be used to classify patients, with moderate performance, into low and high-risk groups for reattendance. We explained our models predictions using SHAP values, a concept developed from coalitional game theory, capable of explaining predictions at an attendance level. We demonstrated how clustering techniques (the UMAP algorithm) can be used to investigate the different sub-groups of explanations present in our patient cohort.

Effects of Sample Size on Plant Single-Cell RNA Profiling

Single-cell RNA (scRNA) profiling or scRNA-sequencing (scRNA-seq) makes it possible to parallelly investigate diverse molecular features of multiple types of cells in a given plant tissue and discover cell developmental processes. In this study, we evaluated the effects of sample size (i.e., cell number) on the outcome of single-cell transcriptome analysis by sampling different numbers of cells from a pool of ~57,000 Arabidopsis thaliana root cells integrated from five published studies. Our results indicated that the most significant principal components could be achieved when 20,000–30,000 cells were sampled, a relatively high reliability of cell clustering could be achieved by using ~20,000 cells with little further improvement by using more cells, 96% of the differentially expressed genes could be successfully identified with no more than 20,000 cells, and a relatively stable pseudotime could be estimated in the subsample with 5000 cells. Finally, our results provide a general guide for optimizing sample size to be used in plant scRNA-seq studies.

Volatile Oil Profile of Prickly Ash (Zanthoxylum) Pericarps from Different Locations in China

Volatile oils of prickly ash (Zanthoxylum) pericarps have various potential biological functions with considerable relevance to food, pharmacological, and industrial applications. The volatile profile of oils extracted from prickly ash pericarps obtained from 72 plantations in China was determined by gas chromatography and mass spectrometry. Several chemometric analyses were used to better understand the volatile oil profile differences among different pericarps and to determine the key factors that affected geographical variations in the main volatile constituents of oils. A total of 47 constituents were detected with D-limonene, alfa-myrcene, and linalool as the most abundant. The volatile profile of pericarp oils was significantly affected by prickly ash species and some environmental factors, and the key factors that affected volatile profile variations for different prickly ash species were diverse. Chemometric analyses based on the volatile oil profile could properly distinguish Z. armatum pericarps from other pericarps. This study provides comprehensive information on the volatile oil profile of pericarps from different prickly ash species and different plantations, and it can be beneficial to a system for evaluating of pericarp quality. Moreover, this study speculates on the key environmental factors that cause volatile oil variations for each species, and can help to obtain better prickly ash pericarp volatile oils by improving the cultivated environments.

A genome-wide meta-analysis uncovers six sequence variants conferring risk of vertigo

Vertigo is the leading symptom of vestibular disorders and a major risk factor for falls. In a genome-wide association study of vertigo (Ncases = 48,072, Ncontrols = 894,541), we uncovered an association with six common sequence variants in individuals of European ancestry, including missense variants in ZNF91, OTOG, OTOGL, and TECTA, and a cis-eQTL for ARMC9. The association of variants in ZNF91, OTOGL, and OTOP1 was driven by an association with benign paroxysmal positional vertigo. Using previous reports of sequence variants associating with age-related hearing impairment and motion sickness, we found eight additional variants that associate with vertigo. Although disorders of the auditory and the vestibular system may co-occur, none of the six genome-wide significant vertigo variants were associated with hearing loss and only one was associated with age-related hearing impairment. Our results uncovered sequence variants associating with vertigo in a genome-wide association study and implicated genes with known roles in inner ear development, maintenance, and disease.

Ancestral Spectrum Analysis With Population-Specific Variants

With the advance of sequencing technology, an increasing number of populations have been sequenced to study the histories of worldwide populations, including their divergence, admixtures, migration, and effective sizes. The variants detected in sequencing studies are largely rare and mostly population specific. Population-specific variants are often recent mutations and are informative for revealing substructures and admixtures in populations; however, computational methods and tools to analyze them are still lacking. In this work, we propose using reference populations and single nucleotide polymorphisms (SNPs) specific to the reference populations. Ancestral information, the best linear unbiased estimator (BLUE) of the ancestral proportion, is proposed, which can be used to infer ancestral proportions in recently admixed target populations and measure the extent to which reference populations serve as good proxies for the admixing sources. Based on the same panel of SNPs, the ancestral information is comparable across samples from different studies and is not affected by genetic outliers, related samples, or the sample sizes of the admixed target populations. In addition, ancestral spectrum is useful for detecting genetic outliers or exploring co-ancestry between study samples and the reference populations. The methods are implemented in a program, Ancestral Spectrum Analyzer (ASA), and are applied in analyzing high-coverage sequencing data from the 1000 Genomes Project and the Human Genome Diversity Project (HGDP). In the analyses of American populations from the 1000 Genomes Project, we demonstrate that recent admixtures can be dissected from ancient admixtures by comparing ancestral spectra with and without indigenous Americans being included in the reference populations.

Disrupting biological sensors of force promotes tissue regeneration in large organisms

Tissue repair and healing remain among the most complicated processes that occur during postnatal life. Humans and other large organisms heal by forming fibrotic scar tissue with diminished function, while smaller organisms respond with scarless tissue regeneration and functional restoration. Well-established scaling principles reveal that organism size exponentially correlates with peak tissue forces during movement, and evolutionary responses have compensated by strengthening organ-level mechanical properties. How these adaptations may affect tissue injury has not been previously examined in large animals and humans. Here, we show that blocking mechanotransduction signaling through the focal adhesion kinase pathway in large animals significantly accelerates wound healing and enhances regeneration of skin with secondary structures such as hair follicles. In human cells, we demonstrate that mechanical forces shift fibroblasts toward pro-fibrotic phenotypes driven by ERK-YAP activation, leading to myofibroblast differentiation and excessive collagen production. Disruption of mechanical signaling specifically abrogates these responses and instead promotes regenerative fibroblast clusters characterized by AKT-EGR1.

Humans and other large mammals heal wounds by forming fibrotic scar tissue with diminished function. Here, the authors show that disrupting mechanotransduction through the focal adhesion kinase pathway in large animals accelerates healing, prevents fibrosis, and enhances skin regeneration.

Paths and timings of the peopling of Polynesia inferred from genomic networks

Polynesia was settled in a series of extraordinary voyages across an ocean spanning one third of the Earth1, but the sequences of islands settled remain unknown and their timings disputed. Currently, several centuries separate the dates suggested by different archaeological surveys2-4. Here, using genome-wide data from merely 430 modern individuals from 21 key Pacific island populations and novel ancestry-specific computational analyses, we unravel the detailed genetic history of this vast, dispersed island network. Our reconstruction of the branching Polynesian migration sequence reveals a serial founder expansion, characterized by directional loss of variants, that originated in Samoa and spread first through the Cook Islands (Rarotonga), then to the Society (Tōtaiete mā) Islands (11th century), the western Austral (Tuha'a Pae) Islands and Tuāmotu Archipelago (12th century), and finally to the widely separated, but genetically connected, megalithic statue-building cultures of the Marquesas (Te Henua 'Enana) Islands in the north, Raivavae in the south, and Easter Island (Rapa Nui), the easternmost of the Polynesian islands, settled in approximately AD 1200 via Mangareva.

B-SOiD, an open-source unsupervised algorithm for identification and fast prediction of behaviors

Studying naturalistic animal behavior remains a difficult objective. Recent machine learning advances have enabled limb localization; however, extracting behaviors requires ascertaining the spatiotemporal patterns of these positions. To provide a link from poses to actions and their kinematics, we developed B-SOiD - an open-source, unsupervised algorithm that identifies behavior without user bias. By training a machine classifier on pose pattern statistics clustered using new methods, our approach achieves greatly improved processing speed and the ability to generalize across subjects or labs. Using a frameshift alignment paradigm, B-SOiD overcomes previous temporal resolution barriers. Using only a single, off-the-shelf camera, B-SOiD provides categories of sub-action for trained behaviors and kinematic measures of individual limb trajectories in any animal model. These behavioral and kinematic measures are difficult but critical to obtain, particularly in the study of rodent and other models of pain, OCD, and movement disorders.

Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex

Cortical circuits are thought to contain a large number of cell types that coordinate to produce behavior. Current in vivo methods rely on clustering of specified features of extracellular waveforms to identify putative cell types, but these capture only a small amount of variation. Here, we develop a new method (WaveMAP) that combines non-linear dimensionality reduction with graph clustering to identify putative cell types. We apply WaveMAP to extracellular waveforms recorded from dorsal premotor cortex of macaque monkeys performing a decision-making task. Using WaveMAP, we robustly establish eight waveform clusters and show that these clusters recapitulate previously identified narrow- and broad-spiking types while revealing previously unknown diversity within these subtypes. The eight clusters exhibited distinct laminar distributions, characteristic firing rate patterns, and decision-related dynamics. Such insights were weaker when using feature-based approaches. WaveMAP therefore provides a more nuanced understanding of the dynamics of cell types in cortical circuits.

Single-cell RNA sequencing reveals distinct cellular factors for response to immunotherapy targeting CD73 and PD-1 in colorectal cancer

BACKGROUND

Although cancer immunotherapy is one of the most effective advanced-stage cancer therapies, no clinically approved cancer immunotherapies currently exist for colorectal cancer (CRC). Recently, programmed cell death protein 1 (PD-1) blockade has exhibited clinical benefits according to ongoing clinical trials. However, ongoing clinical trials for cancer immunotherapies are focused on PD-1 signaling inhibitors such as pembrolizumab, nivolumab, and atezolizumab. In this study, we focused on revealing the distinct response mechanism for the potent CD73 ectoenzyme selective inhibitor AB680 as a promising drug candidate that functions by blocking tumorigenic ATP/adenosine signaling in comparison to current therapeutics that block PD-1 to assess the value of this drug as a novel immunotherapy for CRC.

METHODS

To understand the distinct mechanism of AB680 in comparison to that of a neutralizing antibody against murine PD-1 used as a PD-1 blocker, we performed single-cell RNA sequencing of CD45⁺ tumor-infiltrating lymphocytes from untreated controls (n=3) and from AB680-treated (n=3) and PD-1-blockade-treated murine CRC in vivo models. We also used flow cytometry, Azoxymethane (AOM)/Dextran Sulfate Sodium (DSS) models, and in vitro functional assays to validate our new findings.

RESULTS

We initially observed that the expressions of Nt5e (a gene for CD73) and Entpd1 (a gene for CD39) affect T cell receptor (TCR) diversity and transcriptional profiles of T cells, thus suggesting their critical roles in T cell exhaustion within tumor. Importantly, PD-1 blockade significantly increased the TCR diversity of Entpd1-negative T cells and Pdcd1-positive T cells. Additionally, we determined that AB680 improved the anticancer functions of immunosuppressed cells such as Treg and exhausted T cells, while the PD-1 blocker quantitatively reduced Malat1^high Treg and M2 macrophages. We also verified that PD-1 blockade induced Treg depletion in AOM/DSS CRC in vivo models, and we confirmed that AB680 treatment caused increased activation of CD8⁺ T cells using an in vitro T cell assay.

CONCLUSIONS

The intratumoral immunomodulation of CD73 inhibition is distinct from PD-1 inhibition and exhibits potential as a novel anticancer immunotherapy for CRC, possibly through a synergistic effect when combined with PD-1 blocker treatments. This study may contribute to the ongoing development of anticancer immunotherapies targeting refractory CRC.

AXL inhibition in macrophages stimulates host-versus-leukemia immunity and eradicates naive and treatment resistant leukemia

Acute leukemias are systemic malignancies associated with a dire outcome. Due to low immunogenicity, leukemias display a remarkable ability to evade immune control and are often resistant to checkpoint blockade. Here, we discover that leukemia cells actively establish a suppressive environment to prevent immune attacks by co-opting a signaling axis that skews macrophages towards a tumor promoting tissue repair phenotype, namely the GAS6/AXL axis. Using aggressive leukemia models, we demonstrate that ablation of the AXL receptor specifically in macrophages, or its ligand GAS6 in the environment, stimulates anti-leukemic immunity and elicits effective and lasting NK- and T-cell dependent immune response against naive and treatment resistant leukemia. Remarkably, AXL deficiency in macrophages also enables PD1 checkpoint blockade in PD1-refractory leukemias. Lastly, we provide proof-of-concept that a clinical grade AXL inhibitor can be used in combination with standard of care therapy to cure established leukemia, regardless on AXL expression in malignant cells.

Sub-genic intolerance, ClinVar, and the epilepsies: A whole-exome sequencing study of 29,165 individuals

Both mild and severe epilepsies are influenced by variants in the same genes, yet an explanation for the resulting phenotypic variation is unknown. As part of the ongoing Epi25 Collaboration, we performed a whole-exome sequencing analysis of 13,487 epilepsy-affected individuals and 15,678 control individuals. While prior Epi25 studies focused on gene-based collapsing analyses, we asked how the pattern of variation within genes differs by epilepsy type. Specifically, we compared the genetic architectures of severe developmental and epileptic encephalopathies (DEEs) and two generally less severe epilepsies, genetic generalized epilepsy and non-acquired focal epilepsy (NAFE). Our gene-based rare variant collapsing analysis used geographic ancestry-based clustering that included broader ancestries than previously possible and revealed novel associations. Using the missense intolerance ratio (MTR), we found that variants in DEE-affected individuals are in significantly more intolerant genic sub-regions than those in NAFE-affected individuals. Only previously reported pathogenic variants absent in available genomic datasets showed a significant burden in epilepsy-affected individuals compared with control individuals, and the ultra-rare pathogenic variants associated with DEE were located in more intolerant genic sub-regions than variants associated with non-DEE epilepsies. MTR filtering improved the yield of ultra-rare pathogenic variants in affected individuals compared with control individuals. Finally, analysis of variants in genes without a disease association revealed a significant burden of loss-of-function variants in the genes most intolerant to such variation, indicating additional epilepsy-risk genes yet to be discovered. Taken together, our study suggests that genic and sub-genic intolerance are critical characteristics for interpreting the effects of variation in genes that influence epilepsy.

Assessing single-cell transcriptomic variability through density-preserving data visualization

Nonlinear data visualization methods, such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), summarize the complex transcriptomic landscape of single cells in two dimensions or three dimensions, but they neglect the local density of data points in the original space, often resulting in misleading visualizations where densely populated subsets of cells are given more visual space than warranted by their transcriptional diversity in the dataset. Here we present den-SNE and densMAP, which are density-preserving visualization tools based on t-SNE and UMAP, respectively, and demonstrate their ability to accurately incorporate information about transcriptomic variability into the visual interpretation of single-cell RNA sequencing data. Applied to recently published datasets, our methods reveal significant changes in transcriptomic variability in a range of biological processes, including heterogeneity in transcriptomic variability of immune cells in blood and tumor, human immune cell specialization and the developmental trajectory of Caenorhabditis elegans. Our methods are readily applicable to visualizing high-dimensional data in other scientific domains.

Multilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits

Here, we performed a comprehensive intra-tissue and inter-tissue multilayer network analysis of the human transcriptome. We generated an atlas of communities in gene co-expression networks in 49 tissues (GTEx v8), evaluated their tissue specificity, and investigated their methodological implications. UMAP embeddings of gene expression from the communities (representing nearly 18% of all genes) robustly identified biologically-meaningful clusters. Notably, new gene expression data can be embedded into our algorithmically derived models to accelerate discoveries in high-dimensional molecular datasets and downstream diagnostic or prognostic applications. We demonstrate the generalisability of our approach through systematic testing in external genomic and transcriptomic datasets. Methodologically, prioritisation of the communities in a transcriptome-wide association study of the biomarker C-reactive protein (CRP) in 361,194 individuals in the UK Biobank identified genetically-determined expression changes associated with CRP and led to considerably improved performance. Furthermore, a deep learning framework applied to the communities in nearly 11,000 tumors profiled by The Cancer Genome Atlas across 33 different cancer types learned biologically-meaningful latent spaces, representing metastasis (p < 2.2 × 10⁻¹⁶) and stemness (p < 2.2 × 10⁻¹⁶). Our study provides a rich genomic resource to catalyse research into inter-tissue regulatory mechanisms, and their downstream consequences on human disease.

UMAP as a Dimensionality Reduction Tool for Molecular Dynamics Simulations of Biomacromolecules: A Comparison Study

Proteins are the molecular machines of life. The multitude of possible conformations that proteins can adopt determines their free-energy landscapes. However, the inherently high dimensionality of a protein free-energy landscape poses a challenge to deciphering how proteins perform their functions. For this reason, dimensionality reduction is an active field of research for molecular biologists. The uniform manifold approximation and projection (UMAP) is a dimensionality reduction method based on a fuzzy topological analysis of data. In the present study, the performance of UMAP is compared with that of other popular dimensionality reduction methods such as t-distributed stochastic neighbor embedding (t-SNE), principal component analysis (PCA), and time-structure independent components analysis (tICA) in the context of analyzing molecular dynamics simulations of the circadian clock protein VIVID. A good dimensionality reduction method should accurately represent the data structure on the projected components. The comparison of the raw high-dimensional data with the projections obtained using different dimensionality reduction methods based on various metrics showed that UMAP has superior performance when compared with linear reduction methods (PCA and tICA) and has competitive performance and scalable computational cost.

Application of Uniform Manifold Approximation and Projection (UMAP) in spectral imaging of artworks

This study assesses the potential of Uniform Manifold Approximation and Projection (UMAP) as an alternative tool to t-distributed Stochastic Neighbor Embedding (t-SNE) for the reduction and visualization of visible spectral images of works of art. We investigate the influence of UMAP parameters-such as, correlation distance, minimum embedding distance, as well as number of embedding neighbors- on the reduction and visualization of spectral images collected from Poèmes Barbares (1896), a major work by the French artist Paul Gauguin in the collection of the Harvard Art Museums. The use of a cosine distance metric and number of neighbors equal to 10 preserves both the local and global structure of the Gauguin dataset in a reduced two-dimensional embedding space thus yielding simple and clear groupings of the pigments used by the artist. The centroids of these groups were identified by locating the densest regions within the UMAP embedding through a 2D histogram peak finding algorithm. These centroids were subsequently fit to the dataset by non-negative least square thus forming maps of pigments distributed across the work of art studied. All findings were correlated to macro XRF imaging analyses carried out on the same painting. The described procedure for reduction and visualization of spectral images of a work of art is quick, easy to implement, and the software is opensource thus promising an improved strategy for interrogating reflectance images from complex works of art.

Genetic substructure and complex demographic history of South African Bantu speakers

South Eastern Bantu-speaking (SEB) groups constitute more than 80% of the population in South Africa. Despite clear linguistic and geographic diversity, the genetic differences between these groups have not been systematically investigated. Based on genome-wide data of over 5000 individuals, representing eight major SEB groups, we provide strong evidence for fine-scale population structure that broadly aligns with geographic distribution and is also congruent with linguistic phylogeny (separation of Nguni, Sotho-Tswana and Tsonga speakers). Although differential Khoe-San admixture plays a key role, the structure persists after Khoe-San ancestry-masking. The timing of admixture, levels of sex-biased gene flow and population size dynamics also highlight differences in the demographic histories of individual groups. The comparisons with five Iron Age farmer genomes further support genetic continuity over ~400 years in certain regions of the country. Simulated trait genome-wide association studies further show that the observed population structure could have major implications for biomedical genomics research in South Africa.

LEA 3: Factor models in population genetics and ecological genomics with R

A major objective of evolutionary biology is to understand the processes by which organisms have adapted to various environments, and to predict the response of organisms to new or future conditions. The availability of large genomic and environmental data sets provides an opportunity to address those questions, and the R package LEA has been introduced to facilitate population and ecological genomic analyses in this context. By using latent factor models, the program computes ancestry coefficients from population genetic data and performs genotype-environment association analyses with correction for unobserved confounding variables. In this study, we present new functionalities of LEA, which include imputation of missing genotypes, fast algorithms for latent factor mixed models using multivariate predictors for genotype-environment association studies, population differentiation tests for admixed or continuous populations, and estimation of genetic offset based on climate models. The new functionalities are implemented in version 3.1 and higher releases of the package. Using simulated and real data sets, our study provides evaluations and examples of applications, outlining important practical considerations when analysing ecological genomic data in R.

High throughput screening for expanded CTG repeats in myotonic dystrophy type 1 using melt curve analysis

Background

Myotonic dystrophy type 1 (DM1) is caused by CTG repeat expansions in the DMPK gene and is the most common form of muscular dystrophy. Patients can have long delays from onset to diagnosis, since clinical signs and symptoms are often nonspecific and overlapping with other disorders. Clinical genetic testing by Southern blot or triplet‐primed PCR (TP‐PCR) is technically challenging and cost prohibitive for population surveys.

Methods

Here, we present a high throughput, low‐cost screening tool for CTG repeat expansions using TP‐PCR followed by high resolution melt curve analysis with saturating concentrations of SYBR GreenER dye.

Results

We determined that multimodal melt profiles from the TP‐PCR assay are a proxy for amplicon length stoichiometry. In a screen of 10,097 newborn blood spots, melt profile analysis accurately reflected the tri‐modal distribution of common alleles from 5 to 35 CTG repeats, and identified the premutation and full expansion alleles.

Conclusion

We demonstrate that robust detection of expanded CTG repeats in a single tube can be achieved from samples derived from specimens with minimal template DNA such as dried blood spots (DBS). This technique is readily adaptable to large‐scale testing programs such as population studies and newborn screening programs.

A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance

ForceMyography (FMG) is an emerging competitor to surface ElectroMyography (sEMG) for hand gesture recognition. Most of the state-of-the-art research in this area explores different machine learning algorithms or feature engineering to improve hand gesture recognition performance. This paper proposes a novel signal processing pipeline employing a manifold learning method to produce a robust signal representation to boost hand gesture classifiers’ performance. We tested this approach on an FMG dataset collected from nine participants in 3 different data collection sessions with short delays between each. For each participant’s data, the proposed pipeline was applied, and then different classification algorithms were used to evaluate the effect of the pipeline compared to raw FMG signals in hand gesture classification. The results show that incorporating the proposed pipeline reduced variance within the same gesture data and notably maximized variance between different gestures, allowing improved robustness of hand gestures classification performance and consistency across time. On top of that, the pipeline improved the classification accuracy consistently regardless of different classifiers, gaining an average of 5% accuracy improvement.

Notch Signaling Pathway in Cancer-Review with Bioinformatic Analysis

Simple Summary

The Notch signaling pathway, which controls multiple cell differentiation processes during the embryonic stage and adult life, is associated with carcinogenesis and disease progression. The aim of the present study was to highlight cancer heterogeneity with respect to the Notch pathway. Our analysis concerns the effects of the Notch signaling at different levels, including core components and downstream target genes. We also demonstrate overall and disease-free survival results, pointing out the characteristics of particular Notch components. Depending on tissue context, Notch members can be either oncogenic or suppressive. We observed different expression profile core components and target genes that could be associated with distinct survival of patients. Advances in our understanding of the Notch signaling in cancer are very promising for the development of new treatment strategies for the benefit of patients.

Abstract

Notch signaling is an evolutionarily conserved pathway regulating normal embryonic development and homeostasis in a wide variety of tissues. It is also critically involved in carcinogenesis, as well as cancer progression. Activation of the Notch pathway members can be either oncogenic or suppressive, depending on tissue context. The present study is a comprehensive overview, extended with a bioinformatics analysis of TCGA cohorts, including breast, bladder, cervical, colon, kidney, lung, ovary, prostate and rectum carcinomas. We performed global expression profiling of the Notch pathway core components and downstream targets. For this purpose, we implemented the Uniform Manifold Approximation and Projection algorithm to reduce the dimensions. Furthermore, we determined the optimal cutpoint using Evaluate Cutpoint software to established disease-free and overall survival with respect to particular Notch members. Our results demonstrated separation between tumors and their corresponding normal tissue, as well as between tumors in general. The differentiation of the Notch pathway, at its various stages, in terms of expression and survival resulted in distinct profiles of biological processes such as proliferation, adhesion, apoptosis and epithelial to mesenchymal transition. In conclusion, whether oncogenic or suppressive, Notch signaling is proven to be associated with various types of malignancies, and thus may be of interest as a potential therapeutic target.

IL36 is a critical upstream amplifier of neutrophilic lung inflammation in mice

IL-36, which belongs to the IL-1 superfamily, is increasingly linked to neutrophilic inflammation. Here, we combined in vivo and in vitro approaches using primary mouse and human cells, as well as, acute and chronic mouse models of lung inflammation to provide mechanistic insight into the intercellular signaling pathways and mechanisms through which IL-36 promotes lung inflammation. IL-36 receptor deficient mice exposed to cigarette smoke or cigarette smoke and H1N1 influenza virus had attenuated lung inflammation compared with wild-type controls. We identified neutrophils as a source of IL-36 and show that IL-36 is a key upstream amplifier of lung inflammation by promoting activation of neutrophils, macrophages and fibroblasts through cooperation with GM-CSF and the viral mimic poly(I:C). Our data implicate IL-36, independent of other IL-1 family members, as a key upstream amplifier of neutrophilic lung inflammation, providing a rationale for targeting IL-36 to improve treatment of a variety of neutrophilic lung diseases.

Correspondence-Aware Manifold Learning for Microscopic and Spatial Omics Imaging: A Novel Data Fusion Method Bringing Mass Spectrometry Imaging to a Cellular Resolution

High-dimensional molecular measurements are transforming the field of pathology into a data-driven discipline. While hematoxylin and eosin (H&E) stainings are still the gold standard to diagnose diseases, the integration of microscopic and molecular information is becoming crucial to advance our understanding of tissue heterogeneity. To this end, we propose a data fusion method that integrates spatial omics and microscopic data obtained from the same tissue slide. Through correspondence-aware manifold learning, we can visualize the biological trends observed in the high-dimensional omics data at microscopic resolution. While data fusion enables the detection of elements that would not be detected taking into account the separate data modalities individually, out-of-sample prediction makes it possible to predict molecular trends outside of the measured tissue area. The proposed dimensionality reduction-based data fusion paradigm will therefore be helpful in deciphering molecular heterogeneity by bringing molecular measurements such as mass spectrometry imaging (MSI) to the cellular resolution.

Spatial population genomics of a recent mosquito invasion

Population genomic approaches can characterize dispersal across a single generation through to many generations in the past, bridging the gap between individual movement and intergenerational gene flow. These approaches are particularly useful when investigating dispersal in recently altered systems, where they provide a way of inferring long-distance dispersal between newly established populations and their interactions with existing populations. Human-mediated biological invasions represent such altered systems which can be investigated with appropriate study designs and analyses. Here we apply temporally restricted sampling and a range of population genomic approaches to investigate dispersal in a 2004 invasion of Aedes albopictus (the Asian tiger mosquito) in the Torres Strait Islands (TSI) of Australia. We sampled mosquitoes from 13 TSI villages simultaneously and genotyped 373 mosquitoes at genome-wide single nucleotide polymorphisms (SNPs): 331 from the TSI, 36 from Papua New Guinea (PNG) and four incursive mosquitoes detected in uninvaded regions. Within villages, spatial genetic structure varied substantially but overall displayed isolation by distance and a neighbourhood size of 232-577. Close kin dyads revealed recent movement between islands 31-203 km apart, and deep learning inferences showed incursive Ae. albopictus had travelled to uninvaded regions from both adjacent and nonadjacent islands. Private alleles and a co-ancestry matrix indicated direct gene flow from PNG into nearby islands. Outlier analyses also detected four linked alleles introgressed from PNG, with the alleles surrounding 12 resistance-associated cytochrome P450 genes. By treating dispersal as both an intergenerational process and a set of discrete events, we describe a highly interconnected invasive system.

Population-Based Prevalence of Myotonic Dystrophy Type 1 Using Genetic Analysis of Statewide Blood Screening Program

OBJECTIVE

To determine whether the genetic prevalence of the CTG expansion in the DMPK gene associated with myotonic dystrophy type 1 (DM1) in an unbiased cohort is higher than previously reported population estimates, ranging from 5 to 20 per 100,000 individuals.

METHODS

This study used a cross-sectional cohort of deidentified dried blood spots from the newborn screening program in the state of New York, taken from consecutive births from 2013 to 2014. Blood spots were screened for the CTG repeat expansion in the DMPK gene using triplet-repeat primed PCR and melt curve analysis. Melt curve morphology was assessed by 4 blinded reviewers to identify samples with possible CTG expansion. Expansion of the CTG repeat was validated by PCR fragment sizing using capillary electrophoresis for samples classified as positive or premutation to confirm the result. Prevalence was calculated as the number of samples with CTG repeat size ≥50 repeats compared to the overall cohort.

RESULTS

Of 50,382 consecutive births, there were 24 with a CTG repeat expansion ≥50, consistent with a diagnosis of DM1. This represents a significantly higher DM1 prevalence of 4.76 per 10,000 births (95% confidence interval 2.86-6.67) or 1 in every 2,100 births. There were an additional 96 samples (19.1 per 10,000 or 1 in 525 births) with a CTG expansion in the DMPK gene in the premutation range (CTG)_35-49.

CONCLUSION

The prevalence of individuals with CTG repeat expansions in DMPK is up to 5 times higher than previous reported estimates. This suggests that DM1, with multisystemic manifestations, is likely underdiagnosed in practice.

Visualizing population structure with variational autoencoders

Dimensionality reduction is a common tool for visualization and inference of population structure from genotypes, but popular methods either return too many dimensions for easy plotting (PCA) or fail to preserve global geometry (t-SNE and UMAP). Here we explore the utility of variational autoencoders (VAEs)-generative machine learning models in which a pair of neural networks seek to first compress and then recreate the input data-for visualizing population genetic variation. VAEs incorporate nonlinear relationships, allow users to define the dimensionality of the latent space, and in our tests preserve global geometry better than t-SNE and UMAP. Our implementation, which we call popvae, is available as a command-line python program at github.com/kr-colab/popvae. The approach yields latent embeddings that capture subtle aspects of population structure in humans and Anopheles mosquitoes, and can generate artificial genotypes characteristic of a given sample or population.

Functional Gene Expression Differentiation of the Notch Signaling Pathway in Female Reproductive Tract Tissues-A Comprehensive Review With Analysis

The Notch pathway involves evolutionarily conserved signaling regulating the development of the female tract organs such as breast, ovary, cervix, and uterine endometrium. A great number of studies revealed Notch aberrancies in association with their carcinogenesis and disease progression, the management of which is still challenging. The present study is a comprehensive review of the available literature on Notch signaling during the normal development and carcinogenesis of the female tract organs. The review has been enriched with our analyses of the TCGA data including breast, cervical, ovarian, and endometrial carcinomas concerning the effects of Notch signaling at two levels: the core components and downstream effectors, hence filling the lack of global overview of Notch-driven carcinogenesis and disease progression. Phenotype heterogeneity regarding Notch signaling was projected in two uniform manifold approximation and projection algorithm dimensions, preceded by the principal component analysis step reducing the data burden. Additionally, overall and disease-free survival analyses were performed with the optimal cutpoint determination by Evaluate Cutpoints software to establish the character of particular Notch components in tumorigenesis. In addition to the review, we demonstrated separate models of the examined cancers of the Notch pathway and its targets, although expression profiles of all normal tissues were much more similar to each other than to its cancerous compartments. Such Notch-driven cancerous differentiation resulted in a case of opposite association with DFS and OS. As a consequence, target genes also show very distinct profiles including genes associated with cell proliferation and differentiation, energy metabolism, or the EMT. In conclusion, the observed Notch associations with the female tract malignancies resulted from differential expression of target genes. This may influence a future analysis to search for new therapeutic targets based on specific Notch pathway profiles.

ShinyButchR: Interactive NMF-based decomposition workflow of genome-scale datasets

Non-negative matrix factorization (NMF) has been widely used for the analysis of genomic data to perform feature extraction and signature identification due to the interpretability of the decomposed signatures. However, running a basic NMF analysis requires the installation of multiple tools and dependencies, along with a steep learning curve and computing time. To mitigate such obstacles, we developed ShinyButchR, a novel R/Shiny application that provides a complete NMF-based analysis workflow, allowing the user to perform matrix decomposition using NMF, feature extraction, interactive visualization, relevant signature identification, and association to biological and clinical variables. ShinyButchR builds upon the also novel R package ButchR, which provides new TensorFlow solvers for algorithms of the NMF family, functions for downstream analysis, a rational method to determine the optimal factorization rank and a novel feature selection strategy.

SARS-CoV-2 Receptors and Entry Genes Are Expressed in the Human Olfactory Neuroepithelium and Brain

Reports indicate an association between COVID-19 and anosmia, as well as the presence of SARS-CoV-2 virions in the olfactory bulb. To test whether the olfactory neuroepithelium may represent a target of the virus, we generated RNA-seq libraries from human olfactory neuroepithelia, in which we found substantial expression of the genes coding for the virus receptor angiotensin-converting enzyme-2 (ACE2) and for the virus internalization enhancer TMPRSS2. We analyzed a human olfactory single-cell RNA-seq dataset and determined that sustentacular cells, which maintain the integrity of olfactory sensory neurons, express ACE2 and TMPRSS2. ACE2 protein was highly expressed in a subset of sustentacular cells in human and mouse olfactory tissues. Finally, we found ACE2 transcripts in specific brain cell types, both in mice and humans. Sustentacular cells thus represent a potential entry door for SARS-CoV-2 in a neuronal sensory system that is in direct connection with the brain.

Single-cell profiles of human bone marrow-derived mesenchymal stromal cells after IFN-γ and TNF-α licensing

BACKGROUND

Pre-licensing mesenchymal stromal cells (MSCs) with IFN-γ and TNF-α can empower their immune fate and induce a more effective immune regulation. However, the cellular heterogeneity of MSCs limits our understanding of this inflammatory licensing.

METHODS

The publicly available Gene Expression Omnibus single-cell RNA sequencing (scRNA-seq) data of human bone marrow-derived MSCs with or without IFN-γ and TNF-α licensing were analyzed. Based on the scRNA-seq data and related marker genes, the cell-cycle, stemness, differentiative potencies, and immunomodulate capability of unlicensed and licensed MSCs were compared.

RESULTS

After removing low-quality cells and regressing out the ribosomal gene effects, high-quality data reflecting IFN-γ and TNF-α effect on MSCs were chosen for further analysis. Despite the heterogeneity, pre-licensing didn't influence the cell-cycle and stemness of human bone marrow-derived MSCs. The osteogenesis potencies were decreased, the chondrogenesis potencies were increased while the adipogenesis potencies were stable in licensed MSCs. Licensed MSCs also showed more effective immunomodulate capability including expression of related chemokines, cytokines, surface molecules, and receptors.

CONCLUSION

Collectively, our study showed the expression profiles of human bone marrow-derived unlicensed and licensed MSCs about the cell cycle, stemness, differentiative potencies, and immunomodulate capability at single-cell resolution, which may help the comprehensive understanding about the inflammatory licensing of human bone marrow-derived MSCs and their further clinical application.

Using machine intelligence to uncover Alzheimer’s disease progression heterogeneity

Aim: Research suggests that Alzheimer’s disease (AD) is heterogeneous with numerous subtypes. Through a proprietary interactive ML system, several underlying biological mechanisms associated with AD pathology were uncovered. This paper is an introduction to emerging analytic efforts that can more precisely elucidate the heterogeneity of AD. Methods: A public AD data set (GSE84422) consisting of transcriptomic data of postmortem brain samples from healthy controls (n = 121) and AD (n = 380) subjects was analyzed. Data were processed by an artificial intelligence platform designed to discover potential drug repurposing candidates, followed by an interactive augmented intelligence program. Results: Using perspective analytics, six perspective classes were identified: Class I is defined by TUBB1, ASB4, and PDE5A; Class II by NRG2 and ZNF3; Class III by IGF1, ASB4, and GTSE1; Class IV is defined by cDNA FLJ39269, ITGA1, and CPM; Class V is defined by PDE5A, PSEN1, and NDUFS8; and Class VI is defined by DCAF17, cDNA FLJ75819, and SLC33A1. It is hypothesized that these classes represent biological mechanisms that may act alone or in any combination to manifest an Alzheimer’s pathology. Conclusions: Using a limited transcriptomic public database, six different classes that drive AD were uncovered, supporting the premise that AD is a heterogeneously complex disorder. The perspective classes highlighted genetic pathways associated with vasculogenesis, cellular signaling and differentiation, metabolic function, mitochondrial function, nitric oxide, and metal ion metabolism. The interplay among these genetic factors reveals a more profound underlying complexity of AD that may be responsible for the confluence of several biological factors. These results are not exhaustive; instead, they demonstrate that even within a relatively small study sample, next-generation machine intelligence can uncover multiple genetically driven subtypes. The models and the underlying hypotheses generated using novel analytic methods may translate into potential treatment pathways.

Exploring Additional Valuable Information From Single-Cell RNA-Seq Data

Single-cell RNA-seq (scRNA-seq) technologies are broadly applied to dissect the cellular heterogeneity and expression dynamics, providing unprecedented insights into single-cell biology. Most of the scRNA-seq studies mainly focused on the dissection of cell types/states, developmental trajectory, gene regulatory network, and alternative splicing. However, besides these routine analyses, many other valuable scRNA-seq investigations can be conducted. Here, we first review cell-to-cell communication exploration, RNA velocity inference, identification of large-scale copy number variations and single nucleotide changes, and chromatin accessibility prediction based on single-cell transcriptomics data. Next, we discuss the identification of novel genes/transcripts through transcriptome reconstruction approaches, as well as the profiling of long non-coding RNAs and circular RNAs. Additionally, we survey the integration of single-cell and bulk RNA-seq datasets for deconvoluting the cell composition of large-scale bulk samples and linking single-cell signatures to patient outcomes. These additional analyses could largely facilitate corresponding basic science and clinical applications.

Contrasting signatures of genomic divergence during sympatric speciation

The transition from 'well-marked varieties' of a single species into 'well-defined species'-especially in the absence of geographic barriers to gene flow (sympatric speciation)-has puzzled evolutionary biologists ever since Darwin1,2. Gene flow counteracts the buildup of genome-wide differentiation, which is a hallmark of speciation and increases the likelihood of the evolution of irreversible reproductive barriers (incompatibilities) that complete the speciation process3. Theory predicts that the genetic architecture of divergently selected traits can influence whether sympatric speciation occurs4, but empirical tests of this theory are scant because comprehensive data are difficult to collect and synthesize across species, owing to their unique biologies and evolutionary histories5. Here, within a young species complex of neotropical cichlid fishes (Amphilophus spp.), we analysed genomic divergence among populations and species. By generating a new genome assembly and re-sequencing 453 genomes, we uncovered the genetic architecture of traits that have been suggested to be important for divergence. Species that differ in monogenic or oligogenic traits that affect ecological performance and/or mate choice show remarkably localized genomic differentiation. By contrast, differentiation among species that have diverged in polygenic traits is genomically widespread and much higher overall, consistent with the evolution of effective and stable genome-wide barriers to gene flow. Thus, we conclude that simple trait architectures are not always as conducive to speciation with gene flow as previously suggested, whereas polygenic architectures can promote rapid and stable speciation in sympatry.

Single-cell transcriptome analysis demonstrates inter-patient and intra-tumor heterogeneity in primary and metastatic lung adenocarcinoma

In this study, we performed single-cell transcriptome data analysis of fifty primary and metastatic lung adenocarcinoma (LUAD) samples from the GSE123902 and GSE131907 datasets to determine the landscape of inter-patient and intra-tumoral heterogeneity. The gene expression profiles and copy number variations (CNV) showed significant heterogeneity in the primary and metastatic LUAD samples. We observed upregulation of pathways related to translational initiation, endoplasmic reticulum stress, exosomes, and unfolded protein response in the brain metastasis samples as compared to the primary tumor samples. Pathways related to exosomes, cell adhesion and metabolism were upregulated and the epithelial-to-mesenchymal-transition (EMT) pathway was downregulated in brain metastasis samples from chemotherapy-treated LUAD patients as compared to those from the untreated LUAD patients. Tumor cell subgroups in the brain metastasis samples showed differential expression of genes related to type II alveolar cells, chemoresistance, glycolysis and oxidative phosphorylation (metabolic reprogramming), and EMT. Thus, single-cell transcriptome analysis demonstrated intra-patient and intra-tumor heterogeneity in the regulation of pathways related to tumor progression, chemoresistance and metabolism in the primary and metastatic LUAD tissues. Moreover, our study demonstrates that single cell transcriptome analysis is a potentially useful tool for accurate diagnosis and personalized targeted treatment of LUAD patients.

How do species barriers decay? Concordance and local introgression in mosaic hybrid zones of mussels

The Mytilus complex of marine mussel species forms a mosaic of hybrid zones, found across temperate regions of the globe. This allows us to study 'replicated' instances of secondary contact between closely related species. Previous work on this complex has shown that local introgression is both widespread and highly heterogeneous, and has identified SNPs that are outliers of differentiation between lineages. Here, we developed an ancestry-informative panel of such SNPs. We then compared their frequencies in newly sampled populations, including samples from within the hybrid zones, and parental populations at different distances from the contact. Results show that close to the hybrid zones, some outlier loci are near to fixation for the heterospecific allele, suggesting enhanced local introgression, or the local sweep of a shared ancestral allele. Conversely, genomic cline analyses, treating local parental populations as the reference, reveal a globally high concordance among loci, albeit with a few signals of asymmetric introgression. Enhanced local introgression at specific loci is consistent with the early transfer of adaptive variants after contact, possibly including asymmetric bi-stable variants (Dobzhansky-Muller incompatibilities), or haplotypes loaded with fewer deleterious mutations. Having escaped one barrier, however, these variants can be trapped or delayed at the next barrier, confining the introgression locally. These results shed light on the decay of species barriers during phases of contact.

An optimized quantitative proteomics method establishes the cell type-resolved mouse brain secretome

To understand how cells communicate in the nervous system, it is essential to define their secretome, which is challenging for primary cells because of large cell numbers being required. Here, we miniaturized secretome analysis by developing the "high-performance secretome protein enrichment with click sugars" (hiSPECS) method. To demonstrate its broad utility, hiSPECS was used to identify the secretory response of brain slices upon LPS-induced neuroinflammation and to establish the cell type-resolved mouse brain secretome resource using primary astrocytes, microglia, neurons, and oligodendrocytes. This resource allowed mapping the cellular origin of CSF proteins and revealed that an unexpectedly high number of secreted proteins in vitro and in vivo are proteolytically cleaved membrane protein ectodomains. Two examples are neuronally secreted ADAM22 and CD200, which we identified as substrates of the Alzheimer-linked protease BACE1. hiSPECS and the brain secretome resource can be widely exploited to systematically study protein secretion and brain function and to identify cell type-specific biomarkers for CNS diseases.