Biology subjects

Greco, A.

Publications and source records attributed to Greco, A..

23 records · Page 2Linked to original sources

bioRxiv2020

Hematopoietic stem cells self-renew symmetrically or gradually proceed to differentiation

It is not known whether hematopoietic stem cells (HSCs) undergo symmetric or asymmetric cell divisions in the unperturbed bone marrow. Here, we integrate data from HSC fate mapping and cell-cycle-dependent labeling through mathematical inference and thus gain insight into how HSCs coordinate self-renewal with differentiation. We find that most HSC divisions in adult mice are symmetric self-renewing, replacing HSCs lost by direct differentiation and death, and slowly expanding the HSC population. This expansion maintains constant HSC output to multipotent progenitors (MPPs), despite declining HSC differentiation rate with age. We identify a linear hierarchy of differentiation states between tip HSCs and MPPs, where Tie2-driven HSC fate mapping fully covers the progression of the differentiating cells. A turning point from self-renewal to accelerated cell differentiation occurs between early-stage and late-stage MPPs, just before lineage differentiation becomes manifest in single-cell transcriptomes. This stem cell hierarchy precedes lineage differentiation and may limit mutation accumulation in the hematopoietic system.

systems biology↗

bioRxiv2020

Resolving fate and transcriptome of hematopoietic stem cell clones

Adult bone marrow harbors a mosaic of hematopoietic stem cell (HSC) clones of embryonic origin, and recent work suggests that such clones may have coherent lineage fates. To probe under physiological conditions whether HSC clones with different fates are transcriptionally distinct, we developed PolyloxExpress - a Cre recombinase-dependent DNA substrate for in situ barcoding that allows parallel readout of barcodes and transcriptomes in single cells. We describe differentiation-inactive, multilineage and lineage-restricted HSC clones, find that they reside in distinct regions of the transcriptional landscape of hematopoiesis, and identify corresponding gene signatures. All clone types contain proliferating HSCs, indicating that differentiation-inactive HSCs can undergo symmetric self-renewal. Our work establishes an approach for studying determinants of stem cell fate in vivo and provides molecular evidence for fate coherence of HSC clones.

immunology↗

bioRxiv2020

Use of Bioelectric Impedance Analysis (BIA) as a new method to detect prostate cancer

BackgroundTo determine the accuracy of a novel BIA test endorectal probe. MethodsOne hundred-forty consecutive patient candidates to prostate biopsy and 40 healthy volunteers were selected (NCT03428087). Total PSA and PSA density (PSAD) determinations, digital rectal examination (DRE), and the BIA test were analysed in patients and controls. A 16 cores trans rectal prostate biopsy was performed on all patients with clinical suspicion of PCa after a multiparametric MRI (mMRI) test. The study endpoints were to determine accuracy of BIA test in comparison to PSA, PSAD levels, and mMRI and obtain PCa prediction in candidates to prostate biopsy by BIA test. The Mann-Withney U test, the Wilkoxon rank test, and Holm-Bonferronis method were adopted for statistical analyses, and a computational approach was also applied to differentiate patients with PCa from those with benign disease (BPH). ResultsCombined DRE, TRUS, PSA, and PSAD alone failed to satisfactorily discern patients with PCa from those with BPH (62.86% of discrimination accuracy) and mMRI PIRADS [≥]3 showed a sensitivity of 83% and a specificity of 59%. The accuracy in discerning PCa and BPH increased up to 75% by BIA test (sensitivity 63.33% and specificity 83.75%). ConclusionsThe BIA test is a simple, promising, cheap, and reliable test for PCa non-invasive diagnosis. The novel finger probe may improve PCa detection also in patients with low-risk PCa, thus reducing the need of useless biopsies.

biophysics↗

bioRxiv2019

Molecular Inverse Comorbidity between Alzheimer’s disease and Lung Cancer: new insights from Matrix Factorization

Matrix Factorization (MF) is an established paradigm for large-scale biological data analysis with tremendous potential in computational biology.\n\nWe here challenge MF in depicting the molecular bases of epidemiologically described Disease-Disease (DD) relationships. As use case, we focus on the inverse comorbidity association between Alzheimers disease (AD) and lung cancer (LC), described as a lower than expected probability of developing LC in AD patients. To the day, the molecular mechanisms underlying DD relationships remain poorly explained and their better characterization might offer unprecedented clinical opportunities.\n\nTo this goal, we extend our previously designed MF-based framework for the molecular characterization of DD relationships. Considering AD-LC inverse comorbidity as a case study, we highlight multiple molecular mechanisms, among which the previously identified immune system and mitochondrial metabolism. We then discriminate mechanisms specific to LC from those shared with other cancers through a pancancer analysis. Additionally, new candidate molecular players, such as Estrogen Receptor (ER), CDH1 and HDAC, are pinpointed as factors that might underlie the inverse relationship, opening the way to new investigations. Finally, some lung cancer subtype-specific factors are also detected, suggesting the existence of heterogeneity across patients also in the context of inverse comorbidity.

systems biology↗

bioRxiv2017

Determining the optimal number of independent components for reproducible transcriptomic data analysis

BackgroundIndependent Component Analysis (ICA) is a method that models gene expression data as an action of a set of statistically independent hidden factors. The output of ICA depends on a fundamental parameter: the number of components (factors) to compute. The optimal choice of this parameter, related to determining the effective data dimension, remains an open question in the application of blind source separation techniques to transcriptomic data.\n\nResultsHere we address the question of optimizing the number of statistically independent components in the analysis of transcriptomic data for reproducibility of the components in multiple runs of ICA (within the same or within varying effective dimensions) and in multiple independent datasets. To this end, we introduce ranking of independent components based on their stability in multiple ICA computation runs and define a distinguished number of components (Most Stable Transcriptome Dimension, MSTD) corresponding to the point of the qualitative change of the stability profile. Based on a large body of data, we demonstrate that a sufficient number of dimensions is required for biological interpretability of the ICA decomposition and that the most stable components with ranks below MSTD have more chances to be reproduced in independent studies compared to the less stable ones. At the same time, we show that a transcriptomics dataset can be reduced to a relatively high number of dimensions without losing the interpretability of ICA, even though higher dimensions give rise to components driven by small gene sets.\n\nConclusionsWe suggest a protocol of ICA application to transcriptomics data with a possibility of prioritizing components with respect to their reproducibility that strengthens the biological interpretation. Computing too few components (much less than MSTD) is not optimal for interpretability of the results. The components ranked within MSTD range have more chances to be reproduced in independent studies.

bioinformatics↗