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There is concern about second and subsequent waves of COVID-19 caused by the SARS-CoV-2 coronavirus occurring in communities globally that had an initial disease wave. Metropolitan Houston, TX, with a population of 7 million, is experiencing a massive second disease wave that began in late May 2020. To understand SARS-CoV-2 molecular population genomic architecture and evolution and the relationship between virus genotypes and patient features, we sequenced the genomes of 5,085 SARS-CoV-2 strains from these two waves. Our report provides the first molecular characterization of SARS-CoV-2 strains causing two distinct COVID-19 disease waves. We sequenced the genomes of 5,085 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains causing two coronavirus disease 2019 (COVID-19) disease waves in metropolitan Houston, TX, an ethnically diverse region with 7 million residents. The genomes were from viruses recovered in the earliest recognized phase of the pandemic in Houston and from viruses recovered in an ongoing massive second wave of infections. The virus was originally introduced into Houston many times independently. Virtually all strains in the second wave have a Gly614 amino acid replacement in the spike protein, a polymorphism that has been linked to increased transmission and infectivity. Patients infected with the Gly614 variant strains had significantly higher virus loads in the nasopharynx on initial diagnosis. We found little evidence of a significant relationship between virus genotype and altered virulence, stressing the linkage between disease severity, underlying medical conditions, and host genetics. Some regions of the spike protein—the primary target of global vaccine efforts—are replete with amino acid replacements, perhaps indicating the action of selection. We exploited the genomic data to generate defined single amino acid replacements in the receptor binding domain of spike protein that, importantly, produced decreased recognition by the neutralizing monoclonal antibody CR3022. Our report represents the first analysis of the molecular architecture of SARS-CoV-2 in two infection waves in a major metropolitan region. The findings will help us to understand the origin, composition, and trajectory of future infection waves and the potential effect of the host immune response and therapeutic maneuvers on SARS-CoV-2 evolution. IMPORTANCE There is concern about second and subsequent waves of COVID-19 caused by the SARS-CoV-2 coronavirus occurring in communities globally that had an initial disease wave. Metropolitan Houston, TX, with a population of 7 million, is experiencing a massive second disease wave that began in late May 2020. To understand SARS-CoV-2 molecular population genomic architecture and evolution and the relationship between virus genotypes and patient features, we sequenced the genomes of 5,085 SARS-CoV-2 strains from these two waves. Our report provides the first molecular characterization of SARS-CoV-2 strains causing two distinct COVID-19 disease waves.

Probabilistic topic modelling has become essential in many types of single-cell data analysis. Based on probabilistic topic assignments in each cell, we identify the latent representation of cellular states. A dictionary matrix, consisting of topic-specific gene frequency vectors, provides interpretable bases to be compared with known cell type–specific marker genes and other pathway annotations. However, fitting a topic model on a large number of cells would require heavy computational resources–specialized computing units, computing time and memory. Here, we present a scalable approximation method customized for single-cell RNA-seq data analysis, termed ASAP, short for Annotating a Single-cell data matrix by Approximate Pseudobulk estimation. Our approach is more accurate than existing methods but requires orders of magnitude less computing time, leaving much lower memory consumption. We also show that our approach is widely applicable for atlas-scale data analysis; our method seamlessly integrates single-cell and bulk data in joint analysis, not requiring additional preprocessing or feature selection steps.

Abstract Objectives Our goal was to enhance our next-generation sequencing (NGS) molecular oncology workflow from sequencing to analysis through improvements to our custom-built and previously described NGS application. Methods Over 1 year, we collected feedback regarding workflow pain-points and feature requests from all end users of our NGS application. The application consists of a series of scripted pipelines, a MySQL database, and a Java Graphic User Interface (GUI); the end users include molecular pathologists (MPs), medical technologist/medical laboratory technologists (MTs/MLTs), and the molecular laboratory manager. These feedback data were used to engineer significant changes to the pipelines and software architecture. These architecture changes provided the backbone to a suite of feature enhancements aimed to improve turnaround time, decrease manual processes, and increase efficiency for the molecular laboratory staff and directors. Summary The key software architecture changes include implementing support for multiple environments, refactoring common code in the different pipelines, migrating from a per-run pipeline model to a per-sample pipeline model, and key updates to the MySQL database. These changes enabled development of many technical and user experience improvements. We eliminated the need for the pipelines to be launched manually from the Linux command line. Multiple pipelines can be executed concurrently. We created a per-sample pipeline status monitor. Sample entry is integrated with our Laboratory Information System (LIS) barcodes, thus reducing the possibility of transcription errors. We developed quality assurance reports. Socket-based integration with Integrated Genomics Viewer (IGV) was enhanced. We enabled rapid loading of key alignment data into IGV over a wireless network. Features to support resident and fellow driven variant and gene annotation reporting were developed. Support for additional clinical databases was implemented. Conclusions The designed feature enhancements to our previously reported NGS application have added significant sophistication and safety to our clinical NGS workflow. For example, our NGS consensus conference can be held in a conference room over a wireless network, and a trainee can prepare and present each case without ever leaving the application. To date, we have analyzed 2,540 samples using three different assays (TruSight Myeloid Sequencing Panel, AmpliSeq Cancer Hotspot Panel, GlioSeq) and four sequencing instruments (NextSeq, MiSeq, Proton, PGM) in this application. The code is freely available on GitHub.

Introduction: Manual gating of flow cytometry (FCM) data for marrow cell analysis is a standard approach in current practice, although it is time- and labor-consuming. Recent advances in cytometry technology have led to significant efforts in developing partially or fully automated analysis methods. Although multiple supervised and unsupervised FCM data analysis algorithms have been developed, they have not been widely adopted by the clinical and research laboratories. In this study, we evaluated flowDensity, an open source freely available algorithm. as an automated analysis tool for classification of lymphocyte subsets in the bone marrow biopsy specimens. Materials and Methods: FlowDensity-based gating was applied to 102 normal bone marrow samples and compared with the manual analysis. Independent expression of each cell marker was assessed for comprehensive expression analysis and visualization. Results: Our findings showed a correlation between the manual and flowDensity-based gating in the lymphocyte subsets. However, flowDensity-based gating in the populations with a small number of cells in each cluster showed a low degree of correlation. Comprehensive expression analysis successfully identified and visualized the lymphocyte subsets. Discussion: Our study found that although flowDensity might be a promising method for FCM data analysis, more optimization is required before implementing this algorithm into day-to-day workflow.

Mouse vomeronasal sensory neurons are continuously generated from stem cells and differentiate to express V1R or V2R G-protein coupled receptors (GPCRs), along with their respective Gαi2 or Gαo G-protein subunits. We have previously reported that Gαo-type neurons exhibit elevated expression of endoplasmic reticulum (ER) chaperones and a distinctive hypertrophic, gyroid ER architecture. Here we identify full-length mouse with its expression and localization exclusive to the ER of Gαo neurons. deletion resulted in mice that were deficient in Gαo neuronal activation upon exposure to vomeronasal stimuli and a marked reduction in male-male aggressive behavior. In Cnpy1 mice, Gαo neuron develop normally till birth, but undergo selective, progressive apoptosis during postnatal development, despite normal trafficking of V2R GPCRs to dendritic tips. Immunoprecipitation and mass spectrometry revealed that Cnpy1 associates with V2R GPCRs and other ER chaperones. Together, these findings identify Cnpy1 as a component of an ER chaperone complex essential for Gαo neuron signaling and survival.

In multi-cellular organisms, cell identity and functions are primed and refined through interactions with other surrounding cells. Here, we propose a scalable machine learning method, termed SPURCE, which is designed to systematically ascertain common cell-cell communication patterns embedded in single-cell RNA-seq data. We applied our approach to investigate tumour microenvironments consolidating multiple breast cancer data sets and found seven frequently-observed interaction signatures and underlying gene-gene interaction networks. Our results implicate that a part of tumour heterogeneity, especially within the same subtype, is better understood by differential interaction patterns rather than the static expression of known marker genes.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Revised to address the reviewers.

In multi-cellular organisms, cell identity and functions are primed and refined through interactions with other surrounding cells. Here, we propose a scalable machine learning method, termed SPRUCE, which is designed to systematically ascertain common cell-cell communication patterns embedded in single-cell RNA-seq data. We applied our approach to investigate tumour microenvironments consolidating multiple breast cancer data sets and found seven frequently-observed interaction signatures and underlying gene-gene interaction networks. Our results implicate that a part of tumour heterogeneity, especially within the same subtype, is better understood by differential interaction patterns rather than the static expression of known marker genes.

Probabilistic topic modelling has become essential in many types of single-cell data analysis. Based on probabilistic topic assignments in each cell, we identify the latent representation of cellular states, and topic-specific gene frequency vectors provide interpretable bases to be compared with known cell-type-specific marker genes. However, fitting a topic model on a large number of cells would require heavy computational resources--specialized computing units, computing time and memory. Here, we present a scalable approximation method customized for single-cell RNA-seq data analysis, termed ASAP, short for Annotating Single-cell data by Approximate Pseudobulk estimation. Our approach is more accurate than existing methods but requires orders of magnitude less computing time, leaving much lower memory consumption. We also show that our approach is widely applicable for atlas-scale data analysis; our method seamlessly integrates single-cell and bulk data in joint analysis, not requiring additional preprocessing or feature selection steps.Competing Interest StatementThe authors have declared no competing interest.

The ARTIC Network provides a common resource of PCR primer sequences and recommendations for amplifying SARS-CoV-2 genomes. The initial tiling strategy was developed with the reference genome Wuhan-01, and subsequent iterations have addressed areas of low amplification and sequence drop out. Recently, a new version (V4) was released, based on new variant genome sequences, in response to the realization that some V3 primers were located in regions with key mutations. Herein, we compare the performance of the ARTIC V3 and V4 primer sets with a matched set of 663 SARS-CoV-2 clinical samples sequenced with an Illumina NovaSeq 6000 instrument. We observe general improvements in sequencing depth and quality, and improved resolution of the SNP causing the D950N variation in the spike protein. Importantly, we also find nearly universal presence of spike protein substitution G142D in Delta-lineage samples. Due to the prior release and widespread use of the ARTIC V3 primers during the initial surge of the Delta variant, it is likely that the G142D amino acid substitution is substantially underrepresented among early Delta variant genomes deposited in public repositories. In addition to the improved performance of the ARTIC V4 primer set, this study also illustrates the importance of the primer scheme in downstream analyses. Competing Interest Statement The authors have declared no competing interest. Footnotes * Bioproject ID added.

We sequenced the genomes of 320 SARS-CoV-2 strains from COVID-19 patients in metropolitan Houston, Texas, an ethnically diverse region with seven million residents. These genomes were from the viruses causing infections in the earliest recognized phase of the pandemic affecting Houston. Substantial viral genomic diversity was identified, which we interpret to mean that the virus was introduced into Houston many times independently by individuals who had traveled from different parts of the country and the world. The majority of viruses are apparent progeny of strains derived from Europe and Asia. We found no significant evidence of more virulent viral types, stressing the linkage between severe disease, underlying medical conditions, and perhaps host genetics. We discovered a signal of selection acting on the spike protein, the primary target of massive vaccine efforts worldwide. The data provide a critical resource for assessing virus evolution, the origin of new outbreaks, and the effect of host immune response. Competing Interest Statement The authors have declared no competing interest. Footnotes * Addition of GISAID deposition statement in methods.