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Microbial split-pool ligation transcriptomics (microSPLiT) is a high-throughput single-cell RNA sequencing method for bacteria. With four combinatorial barcoding rounds, microSPLiT can profile transcriptional states in hundreds of thousands of Gram-negative and Gram-positive bacteria in a single experiment without specialized equipment. As bacterial samples are fixed and permeabilized before barcoding, they can be collected and stored ahead of time. During the first barcoding round, the fixed and permeabilized bacteria are distributed into a 96-well plate, where their transcripts are reverse transcribed into cDNA and labeled with the first well-specific barcode inside the cells. The cells are mixed and redistributed two more times into new 96-well plates, where the second and third barcodes are appended to the cDNA via in-cell ligation reactions. Finally, the cells are mixed and divided into aliquot sub-libraries, which can be stored until future use or prepared for sequencing with the addition of a fourth barcode. It takes 4 days to generate sequencing-ready libraries, including 1 day for collection and overnight fixation of samples. The standard plate setup enables single-cell transcriptional profiling of up to 1 million bacterial cells and up to 96 samples in a single barcoding experiment, with the possibility of expansion by adding barcoding rounds. The protocol requires experience in basic molecular biology techniques, handling of bacterial samples and preparation of DNA libraries for next-generation sequencing. It can be performed by experienced undergraduate or graduate students. Data analysis requires access to computing resources, familiarity with Unix command line and basic experience with Python or R., (© 2024. Springer Nature Limited.)

We performed a comprehensive analysis of the transcriptional changes occurring during human induced pluripotent stem cell (hiPSC) differentiation to cardiomyocytes. Using single cell RNA-seq, we sequenced > 20,000 single cells from 55 independent samples representing two differentiation protocols and multiple hiPSC lines. Samples included experimental replicates ranging from undifferentiated hiPSCs to mixed populations of cells at D90 post-differentiation. Differentiated cell populations clustered by time point, with differential expression analysis revealing markers of cardiomyocyte differentiation and maturation changing from D12 to D90. We next performed a complementary cluster-independent sparse regression analysis to identify and rank genes that best assigned cells to differentiation time points. The two highest ranked genes between D12 and D24 (MYH7 and MYH6) resulted in an accuracy of 0.84, and the three highest ranked genes between D24 and D90 (A2M, H19, IGF2) resulted in an accuracy of 0.94, revealing that low dimensional gene features can identify differentiation or maturation stages in differentiating cardiomyocytes. Expression levels of select genes were validated using RNA FISH. Finally, we interrogated differences in cardiac gene expression resulting from two differentiation protocols, experimental replicates, and three hiPSC lines in the WTC-11 background to identify sources of variation across these experimental variables., (© 2021. The Author(s).)

Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. We retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction and also identified unexpected gene expression states, including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to high-throughput analysis of gene expression in bacterial communities that are otherwise not amenable to single-cell analysis, such as natural microbiota., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Fractures are common in individuals with COPD and occur at higher bone mass values than expected. COPD appears to be an important risk factor for bone fragility., Introduction: Patients with chronic obstructive pulmonary disease (COPD) have an increased risk of osteoporosis and fractures, but screening and prophylactic measures to prevent both disorders are often neglected in this population. This case-control study assessed the prevalence of osteopenia, osteoporosis, and fractures in patients with COPD, and identified potential risk factors for fractures in this population., Methods: Overall, 91 patients with COPD (COPD group; COPDG) and 81 age- and sex-matched controls (control group; CG) were assessed with bone mineral density (BMD), thoracic/lumbar spine radiographs, and serum PTH and 25-hydroxyvitamin D (25[OH]D) levels. The occurrence of prior fractures was retrieved from clinical history., Results: The prevalence of total fractures in the COPDG was 57.1% (odds of fracture 4.7 times greater compared with the CG), and the femoral neck T-score emerged as the best predictor of fractures. Compared with the CG, the COPDG had lower spine and femoral BMD (p ≤ 0.01) and 25(OH)D levels (p = 0.01) and 2.6 times greater odds of osteoporosis. Among men, vertebral fractures were more prevalent in the COPDG versus CG (25.9% vs. 6.5%, respectively, p = 0.01). The odds of fracture increased with femoral neck T-scores ≤ - 2.7 in the CG and ≤ - 0.6 in the COPDG., Conclusion: These results add robust evidence to an increased odds of osteoporosis and fractures in COPD. Fractures in the COPDG occurred at higher BMD values than expected, suggesting that COPD may be an independent marker of fracture risk, reinforcing a need for regular osteoporosis screening with BMD measurement and prophylaxis of fractures in patients with this disorder.

Cerebellar malformations are diverse congenital anomalies frequently associated with developmental disability. Although genetic and prenatal non-genetic causes have been described, no systematic analysis has been performed. Here, we present a large-exome sequencing study of Dandy-Walker malformation (DWM) and cerebellar hypoplasia (CBLH). We performed exome sequencing in 282 individuals from 100 families with DWM or CBLH, and we established a molecular diagnosis in 36 of 100 families, with a significantly higher yield for CBLH (51%) than for DWM (16%). The 41 variants impact 27 neurodevelopmental-disorder-associated genes, thus demonstrating that CBLH and DWM are often features of monogenic neurodevelopmental disorders. Though only seven monogenic causes (19%) were identified in more than one individual, neuroimaging review of 131 additional individuals confirmed cerebellar abnormalities in 23 of 27 genetic disorders (85%). Prenatal risk factors were frequently found among individuals without a genetic diagnosis (30 of 64 individuals [47%]). Single-cell RNA sequencing of prenatal human cerebellar tissue revealed gene enrichment in neuronal and vascular cell types; this suggests that defective vasculogenesis may disrupt cerebellar development. Further, de novo gain-of-function variants in PDGFRB, a tyrosine kinase receptor essential for vascular progenitor signaling, were associated with CBLH, and this discovery links genetic and non-genetic etiologies. Our results suggest that genetic defects impact specific cerebellar cell types and implicate abnormal vascular development as a mechanism for cerebellar malformations. We also confirmed a major contribution for non-genetic prenatal factors in individuals with cerebellar abnormalities, substantially influencing diagnostic evaluation and counseling regarding recurrence risk and prognosis., (Copyright © 2019 American Society of Human Genetics. All rights reserved.)

Background: Osteoporosis is a systemic skeletal disease characterized by low bone mass and/or microarchitectural deterioration of bone. Osteoporosis is a risk factor for fractures specially in patients with advanced COPD. The aim of this cross-sectional study was to determine the frequency of osteoporosis and vertebral fracture in COPD patients., Methods: We evaluated 91 COPD patients and compared to 82 healthy subjects (control group) matched for gender and age. We measured the bone mineral density by means of dual energy X-ray absorptiometry (DXA), evaluating the lumbar vertebra (L1-L4), proximal femur and total femur and evaluated vertebral fractures in thoracic and lumbar spine using X-ray. We questioned patients whether they had had any fall that resulted in any fracture in the past five years., Results: Males comprised 60.4% of COPD group and 57.3% of the control group. Mean age was 66.2±9.2 years for COPD group and 64.2±8.8 for the control group. The frequency of osteoporosis in the COPD group was 29.7% and 18.3% in control group (p=0.007). The frequency of vertebral fractures was 18.6% in the COPD group and 9.0% in control group (p=0.06). The frequency of reported falls at resulting in any fracture was 36.3% in the COPD group and 7.3% in control group (p=0.001)., Conclusions: Our data indicate that COPD patients present a high frequency of osteoporosis and falls seem to be an important factor for vertebral fracture., (Copyright © 2018 SEPAR. Publicado por Elsevier España, S.L.U. All rights reserved.)

To facilitate scalable profiling of single cells, we developed split-pool ligation-based transcriptome sequencing (SPLiT-seq), a single-cell RNA-seq (scRNA-seq) method that labels the cellular origin of RNA through combinatorial barcoding. SPLiT-seq is compatible with fixed cells or nuclei, allows efficient sample multiplexing, and requires no customized equipment. We used SPLiT-seq to analyze 156,049 single-nucleus transcriptomes from postnatal day 2 and 11 mouse brains and spinal cords. More than 100 cell types were identified, with gene expression patterns corresponding to cellular function, regional specificity, and stage of differentiation. Pseudotime analysis revealed transcriptional programs driving four developmental lineages, providing a snapshot of early postnatal development in the murine central nervous system. SPLiT-seq provides a path toward comprehensive single-cell transcriptomic analysis of other similarly complex multicellular systems., (Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Force-displacement measurements are taken at different rates with an atomic force microscope to assess the correlation between cell health and cell viscoelasticity in THP-1 cells that have been treated with a novel drug carrier. A variable indentation-rate viscoelastic analysis, VIVA, is employed to identify the relaxation time of the cells that are known to exhibit a frequency dependent stiffness. The VIVA agrees with a fluorescent viability assay. This indicates that dextran-lysozyme drug carriers are biocompatible and deliver concentrated toxic material (rhodamine or silver nanoparticles) to the cytoplasm of THP-1 cells. By modelling the frequency dependence of the elastic modulus, the VIVA provides three metrics of cytoplasmic viscoelasticity: a low frequency modulus, a high frequency modulus and viscosity. The signature of cytotoxicity by rhodamine or silver exposure is a frequency independent twofold increase in the elastic modulus and cytoplasmic viscosity, while the cytoskeletal relaxation time remains unchanged. This is consistent with the known toxic mechanism of silver nanoparticles, where metabolic stress causes an increase in the rigidity of the cytoplasm. A variable indentation-rate viscoelastic analysis is presented as a straightforward method to promote the self-consistent comparison between cells. This is paramount to the development of early diagnosis and treatment of disease.