cell type composition
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Development ◽  
2022 ◽  
Author(s):  
Peter Kozulin ◽  
Rodrigo Suárez ◽  
Qiong-Yi Zhao ◽  
Annalisa Paolino ◽  
Linda J. Richards ◽  
...  

Only mammals evolved a neocortex, which integrates sensory-motor and cognitive functions. Significant diversifications in the cellular composition and connectivity of the neocortex occurred between the two main Therian groups: marsupials and eutherians. However, the developmental mechanisms underlying these diversifications are largely unknown. Here, we compared the neocortical transcriptomes of Sminthopsis crassicaudata, a mouse-sized marsupial, with those of eutherian mice at two developmentally equivalent timepoints corresponding to deeper and upper layer neuron generation. Enrichment analyses revealed more mature gene networks in marsupials at the early stage, which reverted at the later stage, suggesting a more precocious but protracted neuronal maturation program relative to birth timing of cortical layers. We ranked genes expressed in different species and identified important differences in gene expression rankings between species. For example, genes known to be enriched in upper-layer cortical projection neuron subtypes, such as Cux1, Lhx2 and Satb2, likely relating to corpus callosum emergence in eutherians. These results show molecular heterochronies of neocortical development in Theria, and highlight changes in gene expression and cell type composition that may underlie neocortical evolution and diversification.


2021 ◽  
Vol 12 ◽  
Author(s):  
Shivanthan Shanthikumar ◽  
Melanie R. Neeland ◽  
Richard Saffery ◽  
Sarath C. Ranganathan ◽  
Alicia Oshlack ◽  
...  

In epigenome-wide association studies analysing DNA methylation from samples containing multiple cell types, it is essential to adjust the analysis for cell type composition. One well established strategy for achieving this is reference-based cell type deconvolution, which relies on knowledge of the DNA methylation profiles of purified constituent cell types. These are then used to estimate the cell type proportions of each sample, which can then be incorporated to adjust the association analysis. Bronchoalveolar lavage is commonly used to sample the lung in clinical practice and contains a mixture of different cell types that can vary in proportion across samples, affecting the overall methylation profile. A current barrier to the use of bronchoalveolar lavage in DNA methylation-based research is the lack of reference DNA methylation profiles for each of the constituent cell types, thus making reference-based cell composition estimation difficult. Herein, we use bronchoalveolar lavage samples collected from children with cystic fibrosis to define DNA methylation profiles for the four most common and clinically relevant cell types: alveolar macrophages, granulocytes, lymphocytes and alveolar epithelial cells. We then demonstrate the use of these methylation profiles in conjunction with an established reference-based methylation deconvolution method to estimate the cell type composition of two different tissue types; a publicly available dataset derived from artificial blood-based cell mixtures and further bronchoalveolar lavage samples. The reference DNA methylation profiles developed in this work can be used for future reference-based cell type composition estimation of bronchoalveolar lavage. This will facilitate the use of this tissue in studies examining the role of DNA methylation in lung health and disease.


Biometrics ◽  
2021 ◽  
Author(s):  
Hillary M. Heiling ◽  
Douglas R. Wilson ◽  
Naim U. Rashid ◽  
Wei Sun ◽  
Joseph G. Ibrahim

2021 ◽  
Author(s):  
Alba Almazan ◽  
Cagri Cevrim ◽  
Jacob M Musser ◽  
Michalis Averof ◽  
Mathilde Paris

Animals can regenerate complex organs, yet this frequently results in imprecise replicas of the original structure. In the crustacean Parhyale, embryonic and regenerating legs differ in gene expression dynamics but produce apparently similar mature structures. We examine the fidelity of Parhyale leg regeneration using complementary approaches to investigate microanatomy, sensory function, cellular composition and cell molecular profiles. We find that regeneration precisely replicates the complex microanatomy and spatial distribution of external sensory organs, and restores their sensory function. Single-nuclei sequencing shows that regenerated and uninjured legs are indistinguishable in terms of cell type composition and transcriptional profiles. This remarkable fidelity highlights the ability of organisms to achieve identical outcomes via distinct processes.


2021 ◽  
Author(s):  
Yunhee Jeong ◽  
Reka Toth ◽  
Marlene Ganslmeier ◽  
Kersten Breuer ◽  
Christoph Plass ◽  
...  

DNA methylation sequencing is becoming increasingly popular, yielding genome-wide methylome data at single-base pair resolution through the novel cost- and labor-optimized protocols. It has tremendous potential for cell-type heterogeneity analysis, particularly in tumors, due to intrinsic read-level information. Although diverse deconvolution methods were developed to infer cell-type composition based on bulk sequencing-based methylomes, their systematic evaluation has not been performed so far. Here, we thoroughly review and evaluate five previously published deconvolution methods: Bayesian epiallele detection (BED), PRISM, csmFinder + coMethy, ClubCpG and MethylPurify, together with two array-based methods, MeDeCom and Houseman as a comparison group. Sequencing-based deconvolution methods consist of two main steps, informative region selection and cell-type composition estimation. Accordingly, we individually assessed the performance of each step and demonstrated the impact of the former step upon the performance of the following one. In conclusion, we demonstrate the best method showing the highest accuracy in different samples, and infer factors affecting cell-type deconvolution performance according to the number of cell types in the mixture. We found that cell-type deconvolution performance is influenced by different factors according to the number of components in the mixture. Whereas selecting similar genomic regions to DMRs generally contributed to increasing the performance in bi-component mixtures, the uniformity of cell-type distribution showed a high correlation with the performance in five cell-type bulk analyses.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Alessandro Fiorenzano ◽  
Edoardo Sozzi ◽  
Marcella Birtele ◽  
Janko Kajtez ◽  
Jessica Giacomoni ◽  
...  

AbstractThree-dimensional brain organoids have emerged as a valuable model system for studies of human brain development and pathology. Here we establish a midbrain organoid culture system to study the developmental trajectory from pluripotent stem cells to mature dopamine neurons. Using single cell RNA sequencing, we identify the presence of three molecularly distinct subtypes of human dopamine neurons with high similarity to those in developing and adult human midbrain. However, despite significant advancements in the field, the use of brain organoids can be limited by issues of reproducibility and incomplete maturation which was also observed in this study. We therefore designed bioengineered ventral midbrain organoids supported by recombinant spider-silk microfibers functionalized with full-length human laminin. We show that silk organoids reproduce key molecular aspects of dopamine neurogenesis and reduce inter-organoid variability in terms of cell type composition and dopamine neuron formation.


2021 ◽  
Author(s):  
Belinda Phipson ◽  
Choon Boon Sim ◽  
Enzo R. Porrello ◽  
Alex W Hewitt ◽  
Joseph Powell ◽  
...  

Single cell RNA Sequencing (scRNA-seq) has rapidly gained popularity over the last few years for profiling the transcriptomes of thousands to millions of single cells. To date, there are more than a thousand software packages that have been developed to analyse scRNA-seq data. These focus predominantly on visualization, dimensionality reduction and cell type identification. Single cell technology is now being used to analyse experiments with complex designs including biological replication. One question that can be asked from single cell experiments which has not been possible to address with bulk RNA-seq data is whether the cell type proportions are different between two or more experimental conditions. As well as gene expression changes, the relative depletion or enrichment of a particular cell type can be the functional consequence of disease or treatment. However, cell type proportions estimates from scRNA-seq data are variable and statistical methods that can correctly account for different sources of variability are needed to confidently identify statistically significant shifts in cell type composition between experimental conditions. We present propeller, a robust and flexible method that leverages biological replication to find statistically significant differences in cell type proportions between groups. The propeller method is publicly available in the open source speckle R package (https://github.com/Oshlack/speckle).


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lorena Pantano ◽  
George Agyapong ◽  
Yang Shen ◽  
Zhu Zhuo ◽  
Francesc Fernandez-Albert ◽  
...  

AbstractNon-alcoholic fatty liver disease (NAFLD) is the most common cause of liver disease worldwide. In adults with NAFLD, fibrosis can develop and progress to liver cirrhosis and liver failure. However, the underlying molecular mechanisms of fibrosis progression are not fully understood. Using total RNA-Seq, we investigated the molecular mechanisms of NAFLD and fibrosis. We sequenced liver tissue from 143 adults across the full spectrum of fibrosis stage including those with stage 4 fibrosis (cirrhosis). We identified gene expression clusters that strongly correlate with fibrosis stage including four genes that have been found consistently across previously published transcriptomic studies on NASH i.e. COL1A2, EFEMP2, FBLN5 and THBS2. Using cell type deconvolution, we estimated the loss of hepatocytes versus gain of hepatic stellate cells, macrophages and cholangiocytes with advancing fibrosis stage. Hepatocyte-specific functional analysis indicated increase of pro-apoptotic pathways and markers of bipotent hepatocyte/cholangiocyte precursors. Regression modelling was used to derive predictors of fibrosis stage. This study elucidated molecular and cell composition changes associated with increasing fibrosis stage in NAFLD and defined informative gene signatures for the disease.


2021 ◽  
Author(s):  
Adir Katz ◽  
Renaud Gaujoux ◽  
Hadas Orly ◽  
Elina Starosvetsky ◽  
Roye Rozov ◽  
...  

AbstractCells are the quanta unit of biology and their relative composition in a tissue is the prime driver of bulk tissue gene expression variation. When there is no cell information, deconvolution is an effective tool to achieve cell resolution, which provides important information for learning disease complexity and its interactions with treatments, drugs and/or the environment in a wide variety of contexts. Here we present CytoPro, a production-level tissue and condition-specific deconvolution platform, based on a large collection of human tissue-specific signatures derived from single and sorted cells. CytoPro infer per-sample multiple cell-type composition, given input bulk gene expression. CytoPro includes a rigorous QC pipeline for learning, generating and selecting signatures and performs internal automated validation using multiple QC test criteria including: Comparison to ground truth cytometry and pure sorted cells data, performance evaluation using simulated data including robustness to noise as well as agreement with biological expectations in validation datasets regarding genes and cells. We demonstrate that CytoPro outperforms existing deconvolution tools, in both accuracy and robustness. By exploring multiple datasets with predefined disease phenotypes, and analyzing a use-case of biological treatment response, we show the ability of CytoPro to flush out relevant cell biology in real pathological conditions.


2021 ◽  
Author(s):  
Wenjing Ma ◽  
Sumeet Sharma ◽  
Peng Jin ◽  
Shannon L Gourley ◽  
Zhaohui Qin

The rapid proliferation of single-cell RNA-sequencing (scRNA-seq) datasets have revealed cell heterogeneity at unprecedented scales. Several deconvolution methods have been developed to decompose bulk experiments to reveal cell type contributions. However, these methods lack power in identifying the accurate cell type composition when having a considerable amount of sub-cell types in the reference dataset. Here, we present LRcell, a R Bioconductor package (http://bioconductor.org/packages/release/bioc/html/LRcell.html) aiming to identify specific sub-cell type(s) that drives the changes observed in a bulk RNA-seq differential gene expression experiment. In addition, LRcell provides pre-embedded marker genes computed from putative single-cell RNA-seq experiments as options to execute the analyses.


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