Mutualistic bacteria infect most eukaryotic species in nearly every biome. Nonetheless, two dilemmas remain unresolved about bacterial-eukaryote mutualisms: how do mutualist phenotypes originate in bacterial lineages and to what degree do mutualists traits drive or hinder bacterial diversification? Here, we reconstructed the phylogeny of the hyperdiverse phylum Proteobacteria to investigate the origins and evolutionary diversification of mutualistic bacterial phenotypes. Our ancestral state reconstructions (ASRs) inferred a range of 34-39 independent origins of mutualist phenotypes in Proteobacteria, revealing the surprising frequency with which host-beneficial traits have evolved in this phylum. We found proteobacterial mutualists to be more often derived from parasitic than from free-living ancestors, consistent with the untested paradigm that bacterial mutualists most often evolve from pathogens. Strikingly, we inferred that mutualists exhibit a negative net diversification rate (speciation minus extinction), which suggests that mutualism evolves primarily via transitions from other states rather than diversification within mutualist taxa. Moreover, our ASRs infer that proteobacterial mutualist lineages exhibit a paucity of reversals to parasitism or to free-living status. This evolutionary conservatism of mutualism is contrary to long-standing theory, which predicts that selection should often favour mutants in microbial mutualist populations that exploit or abandon more slowly evolving eukaryotic hosts.

<p class="abstract"><span lang="EN-IN">Monoclonal gammopathy is clonal proliferation and accumulation of immunoglobulin producing B-cells. A variety of skin disorders are associated with an increased level of monoclonal immunoglobulin proteins. Synonyms such as monoclonal gammopathies, paraproteinemias, plasma cell dyscrasias and dysproteinemias are used to designate gammaglobinopathies. Here in we report a case of POEMS (polyneuropathy, organomegaly, endocrinopathy, monoclonal gammopathy and skin changes) syndrome presenting with sclerodermoid features.</span></p>

Obesity can weaken the body’s immune system and trigger chronic inflammation. To understand the root cause of this problem, we used single cell sequencing to examine thousands of immune cells isolated from primary and secondary lymphoid organs as well as adipose tissue and compared a diet-induced obesity mouse model with control mice. Cells were stained with 30 DNA-barcoded antibodies from BD™ AbSeq reagents to enable multiomic analysis, i.e. examining protein alongside mRNA expression in tandem. We also utilized DNA-barcoded universal antibodies from the BD™ Single-Cell Multiplexing Kit, which allowed us to combine 8 samples from different mice and tissue types into a single pooled sample, significantly reducing experimental scale and cost while eliminating potential batch effects. The pooled samples were loaded on the BD Rhapsody™ system to perform cell lysis and individual mRNA and cell barcoding, allowing measurement of ~400 immune-related mRNAs and 30 proteins at the single cell level. We were able to de-multiplex the pooled samples with high specificity after sequencing. The targeted mRNA and AbSeq panel provided robust clustering of immune cell types and showed that genes related to critical immune responses, including inflammation and lymphocyte activation, are differentially regulated in specific immune-cell subsets in obese mouse. Using this multiomic analysis of genes differentially regulated in immune cells from different tissues, we propose a model to explain the immuno-phenotype we observed in obese mouse. For Research Use Only. Not for use in diagnostic or therapeutic procedures. BD, the BD Logo, and Rhapsody are trademarks of Becton, Dickinson and Company. © 2019 BD and its subsidiaries. All rights reserved.

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Diverse immune populations reside in non-lymphoid organs and contribute to immune defense and tissue homeostasis. However, these cells are often hard to study due to low cell abundance and high heterogeneity. Recent advancements in single-cell sequencing technology provides a powerful high parameter tool to study these peripheral immune populations. But current single cell experiments are costly and limited by sample throughput. To address these limitations, we have developed a novel sample multiplexing approach for high throughput single-cell sequencing. In this study, we performed single-cell sequencing analysis of thousands of immune cells isolated from peripheral tissues in mice, and utilized a DNA barcoded universal antibody to sample multiplex up to 12 samples in a single experiment. This allowed us to combine samples from different mice and tissue types into a single pooled sample, significantly reducing experimental scale and cost, while eliminating potential batch effects. The sample pool was captured on the BD Rhapsody™ system and a targeted assay was performed to measure gene expression of ~400 genes. We were able to de-multiplex the pooled samples with high specificity after sequencing. The targeted gene panel provided robust clustering of the major immune cell types, enabling us to perform immuno-profiling and compare gene expression of major cell populations across different tissues. We observed distinct tissue-specific expression profiles of major immune populations, and further investigation of the differentially expressed genes may provide better understanding of the interactions between these tissue immune populations and their local environment.

Single cell RNA-sequencing (scRNA-seq) is a powerful tool for understanding the sample heterogeneity of individual cells. Many methods rely on whole transcriptome analysis (WTA) to get a snapshot of the entire cellular landscape. Although WTA analysis can be used to discover novel biomarkers, this technique can be expensive. To enable scaling of experiments, WTA data can be mined to design targeted gene panels. To showcase this, we used single cell sequencing to examine thousands of B cells isolated from the bone marrow and peripheral blood of chronic lymphocytic leukemia (CLL) and healthy donors. B cells that had been sorted using the BD FACSMelody™ cell sorter were multiplexed using the BD™ Human Single-Cell Multiplexing Kit and pooled before being processed on the BD Rhapsody™ system, thereby minimizing batch effects. The resulting data was mined to design a panel of differentially expressed genes between CLL and healthy B cells that could be used for subsequent CLL phenotyping. By combining this panel with the BD Rhapsody™ Immune Response Panel (together comprising ~500 mRNAs), along with 36 DNA-barcoded BD™ AbSeq antibodies, we were able to simultaneously analyze mRNA and protein targets from a new subset of CLL and healthy B cells for additional high-resolution analysis. This study showcases the power of using WTA data to design specific gene panels that can be used alone or in combination with existing targeted panels for routine and cost-effective transcriptional profiling at a single cell level. For Research Use Only. Not for use in diagnostic or therapeutic procedures. BD, the BD Logo, FACSMelody, and Rhapsody are trademarks of Becton, Dickinson and Company. © 2019 BD and its subsidiaries. All rights reserved.

The immune system consists of complex gene regulatory networks that allow a rapid transition of different cellular states during an immune response. Cell-surface marker analysis using flow cytometry or single cell RNA-seq has allowed characterization of immune subpopulations and a greater understanding of the complexity of immune cells. However, restrictions on protein-only or RNA-only analysis can greatly limit the understanding of how genes are regulated in cells. For example, many cell surface markers – such as CD4 in T cells – have thousands of protein copies per cell, yet is fueled by a small number of mRNA transcripts. To bridge the understanding of protein and mRNA expression differences, we used BD™ AbSeq on the Rhapsody™ platform to provide digital quantification of both protein and mRNA expression level in single cells. An oligo-conjugated antibody panel against immune-relevant cell-surface markers was created and used for this multi-omic gene expression profiling effort. This approach is coupled with mRNA analysis using the BD Rhapsody Immune Response Panel, a targeted method of RNA-seq that allows a higher sensitivity of immune markers than conventional whole transcriptome assays. We studied the dynamics of early T cell activation in vitro to understand this response on transcriptional, post-transcriptional, and translational levels. Different time points following anti-CD3 and anti-CD28 treatment were collected and multiplexed on to BD Rhapsody cartridge for single cell capture and analysis. Using AbSeq on BD Rhapsody, we were able to detect the difference in mRNA and protein levels of crucial markers, allowing us to dissect the intricate gene regulatory pathways during an immune response in a single cell level.

Single-cell RNA sequencing (scRNA-seq) has the ability to classify each cell and determine the transcriptomic profile of specific cell types and cells of a given disease state; however, sensitivity of the gene count for each cell can be a critical component to the success of a single-cell study. The recently introduced SMART-Seq Single Cell PLUS Kit (SSsc PLUS) claims to provide higher sensitivity and reproducibility versus popular methods for the sequencing analysis of single cells. Here, the cDNAgeneration component of the kit, SMART-Seq Single Cell Kit (SSsc), was compared with the popular homebrew protocol, Smart-seq2, and its update, Smart-seq3. The SMART-Seq Library Prep Kit from SSsc PLUS was benchmarked against a commonly used scRNA-seq library preparation method, Illumina Nextera XT. Finally, the SSsc chemistry was tested in both full and fractional volumes on 2 popular liquid-handler devices to investigate whether the high sensitivity was maintained in miniaturization.We demonstrate that SSsc PLUS outperforms these other full-length methods in convenience, sensitivity, gene identification, and reproducibility while also offering full compatibility with automation platforms.