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Methods for quantifying gene expression 1 and chromatin accessibility 2 in single cells are well established, but single-cell analysis of chromatin regions with specific histone modifications has been technically challenging. In this study, we adapted the CUT&Tag method 3 to scalable nanowell and droplet-based single-cell platforms to profile chromatin landscapes in single cells (scCUT&Tag) from complex tissues and during the differentiation of human embryonic stem cells. We focused on profiling polycomb group (PcG) silenced regions marked by histone H3 Lys27 trimethylation (H3K27me3) in single cells as an orthogonal approach to chromatin accessibility for identifying cell states. We show that scCUT&Tag profiling of H3K27me3 distinguishes cell types in human blood and allows the generation of cell-type-specific PcG landscapes from heterogeneous tissues. Furthermore, we used scCUT&Tag to profile H3K27me3 in a patient with a brain tumor before and after treatment, identifying cell types in the tumor microenvironment and heterogeneity in PcG activity in the primary sample and after treatment. An improved method for single-cell analysis of histone modifications is applied to stem cell differentiation and cancer.

Glioblastoma (GBM) is a heterogeneous tumor made up of cell states that evolve over time. Here, we modeled tumor evolutionary trajectories during standard-of-care treatment using multi-omic single-cell analysis of a primary tumor sample, corresponding mouse xenografts subjected to standard of care therapy, and recurrent tumor at autopsy. We mined the multi-omic data with single-cell SYstems Genetics Network AnaLysis (scSYGNAL) to identify a network of 52 regulators that mediate treatment-induced shifts in xenograft tumor-cell states that were also reflected in recurrence. By integrating scSYGNAL-derived regulatory network information with transcription factor accessibility deviations derived from single-cell ATAC-seq data, we developed consensus networks that modulate cell state transitions across subpopulations of primary and recurrent tumor cells. Finally, by matching targeted therapies to active regulatory networks underlying tumor evolutionary trajectories, we provide a framework for applying single-cell-based precision medicine approaches to an individual patient in a concurrent, adjuvant, or recurrent setting.

An improved method for single-cell analysis of histone modifications is applied to stem cell differentiation and cancer.

An improved method for single-cell analysis of histone modifications is applied to stem cell differentiation and cancer.

Single-cell analysis has become a powerful approach for the molecular characterization of complex tissues. Methods for quantifying gene expression1 and chromatin accessibility2 of single cells are now well-established, but analysis of chromatin regions with specific histone modifications has been technically challenging. Here, we adapt the recently published CUT&Tag method3 to scalable single-cell platforms to profile chromatin landscapes in single cells (scCUT&Tag) from complex tissues. We focus on profiling Polycomb Group (PcG) silenced regions marked by H3K27 trimethylation (H3K27me3) in single cells as an orthogonal approach to chromatin accessibility for identifying cell states. We show that scCUT&Tag profiling of H3K27me3 distinguishes cell types in human blood and allows the generation of cell-type-specific PcG landscapes from heterogeneous tissues. Furthermore, we use scCUT&Tag to profile H3K27me3 in a brain tumor patient before and after treatment, identifying cell types in the tumor microenvironment and heterogeneity in PcG activity in the primary sample and after treatment. Competing Interest Statement The authors have declared no competing interest. Footnotes * We have prepared a revised manuscript which incorporated biologic and technical replicates of H3K27me3 data and from a second histone mark (K27ac) in PBMCs. We have also provided deeper insight into data quality of the tumor samples.

Abstract Glioblastoma (GBM) is a heterogeneous tumor made up of cell states that evolve over time. Here, we modeled tumor evolutionary trajectories during standard-of-care treatment using multimodal single-cell analysis of a primary tumor sample, corresponding mouse xenografts subjected to standard of care therapy, and recurrent tumor at autopsy. We mined the multimodal data with single cell SYstems Genetics Network AnaLysis (scSYGNAL) to identify a network of 52 regulators that mediate treatment-induced shifts in xenograft tumor-cell states that were also reflected in recurrence. By integrating scSYGNAL-derived regulatory network information with transcription factor accessibility deviations derived from single-cell ATAC-seq data, we developed consensus networks that regulate subpopulations of primary and recurrent tumor cells. Finally, by matching targeted therapies to active regulatory networks underlying tumor evolutionary trajectories, we provide a framework for applying single-cell-based precision medicine approaches in a concurrent, neo-adjuvant, or recurrent setting. Summary Inference of mechanistic drivers of therapy-induced evolution of glioblastoma at single cell resolution using RNA-seq and ATAC-seq from patient samples and model systems undergoing standard-of-care treatment informs strategy for identification of tumor evolutionary trajectories and possible cell state-directed therapeutics. Competing Interest Statement The authors have declared no competing interest.

As global temperatures rise, large amounts of carbon sequestered in permafrost are becoming available for microbial degradation. Accurate prediction of carbon gas emissions from thawing permafrost is limited by our understanding of these microbial communities. Here we use metagenomic sequencing of 214 samples from a permafrost thaw gradient to recover 1,529 metagenome-assembled genomes, including many from phyla with poor genomic representation. These genomes reflect the diversity of this complex ecosystem, with genus-level representatives for more than sixty per cent of the community. Meta-omic analysis revealed key populations involved in the degradation of organic matter, including bacteria whose genomes encode a previously undescribed fungal pathway for xylose degradation. Microbial and geochemical data highlight lineages that correlate with the production of greenhouse gases and indicate novel syntrophic relationships. Our findings link changing biogeochemistry to specific microbial lineages involved in carbon processing, and provide key information for predicting the effects of climate change on permafrost systems. Analysis of more than 1,500 microbial genomes sheds light on the processing of carbon released as permafrost thaws.

Using environmental DNA (eDNA)-based tools, we examined sediments underlying a ~ 1.25 hectare commercial kelp farm in the Gulf of Maine growing sugar kelp ( Saccharina latissima ) for two farming seasons, post-harvest. Two eDNA methods were used: a newly designed S. latissima -specific digital polymerase chain reaction (dPCR) assay targeting the cytochrome oxidase subunit I (COI) mitochondrial gene, as well as metabarcoding for the 16S and 18S ribosomal RNA (rRNA) genes, to examine overall bacterial, archaeal, and eukaryotic diversity. Sediment carbon and nitrogen content was analyzed using isotope ratio mass spectrometry (IRMS) as more traditional indicators of potential kelp biomass-derived nutrient enrichment in the benthos. When targeted sampling sites were added inside the footprint of the farm lease area in year two of the study, dPCR data showed subtle but significant differences between sediment samples inside and outside of the farm, with mean S. latissima COI gene copies from cores taken inside the farm being ~8% greater than mean values outside the farm. The highest COI copy numbers in marine sediments were from sites with observed accumulation of kelp biomass, while there was no conclusive difference in carbon and nitrogen content of those same sediment samples. Metabarcoding data also revealed subtle differences in taxa associated with sediments inside and outside the farm. For example, microbial taxa that correlated with kelp eDNA from cores within the farm included the families Rhodothermaceae, Rubritaleaceae, Flavobacteriaceae, Prolixibacteraceae, Nitrosomonadaceae, Nitrincolaceae and Rubinisphaeraceae . However, the majority of the above taxa were low in relative abundance, with only Flavobacteriaceae ranking among the top 30 most abundant and prevalent families in these sediments. In summary, this study demonstrates the sensitivity and specificity of eDNA tools to detect potential ecological and anthropogenic effects in marine sediments, beyond that of bulk nutrient and stable isotope analyses.

Three structural features of lipid A (addition of palmitate [C16 fatty acid], addition of aminoarabinose [positively charged amino sugar residue], and retention of 3-hydroxydecanoate [3-OH C10 fatty acid]) were determined for Pseudomonas aeruginosa isolates from patientswith cystic fibrosis (CF; n = 86), from the environment (n = 13), and from patients with other conditions (n = 14). Among P. aeruginosa CF isolates, 100% had lipid A with palmitate, 24.6% with aminoarabinose, and 33.3% retained 3-hydroxydecanoate. None of the isolates from the environment or from patients with other conditions displayed these modifications. These results indicate that unique lipid A modifications occur in clinical P. aeruginosa CF isolates.

Malnutrition is a major risk factor of cardiovascular and metabolic diseases and therefore the importance of good dietary practices and balanced diet cannot be overemphasized. University students tend to have poor eating practices which is related to nutritional status. The objective of our study was to assess the dietary practices of medical students, determine the prevalence of malnutrition among medical students and factors associated with malnutrition. We carried out a cross-sectional study from December 2013 to March 2014 involving 203 consenting students in the Faculty of Medicine and Biomedical Sciences of the University of Yaoundé I, Faculties of Health Sciences of the Universities of Bamenda and Buea. A three-part questionnaire (socio-demographic profile, eating practices, and anthropometric parameters). Data was analysed using SPSS 18.0. Frequencies and percentages were determined for categorical variables. Means and standard deviations (mean ± SD) were calculated for continuous variables. Fischer's exact test was used to compare the categorical variables. Statistical significance was set at p ≤ 0.05. Males constituted 44.3% of respondents. The mean age was 20.8 ± 1.6yrs. Most students had a monthly allowance of less than 20 000frs (34 USD) and 59.1% lived alone. Most students (49.8%) reported taking two meals a day with breakfast being the most skipped meal while supper was the meal most consumed by students. Snacking was common among these students as 40.8% admitted consuming snacks daily. Daily intake of milk, fruits, vegetable and meat were low (6.2%, 4.3%, 20.0% and 21.3% respectively). The BMI status of students was associated with gender (p=0.026). Our findings showed a high prevalence of malnutrition of 29.4% based on BMI (underweight 4.9%, overweight 21.6% and obesity 3.0%) among second year medical students of these three state universities. Irregular meals, meal skipping, low fruit, vegetable and milk consumption, high candy, fried foods and alcohol intakes were found to be poor eating practices frequent among these students. Our findings therefore suggest the need for coordinated efforts to promote healthy eating habits among medical students in general and female medical students in particular (and by extension youths in general) as a means of curbing malnutrition among youths.