in-vitro-selection-engineered-transcriptional-repressors-for-targeted

<em>In Vitro</em> Selection of Engineered Transcriptional Repressors for Targeted Epigenetic Silencing

Gene inactivation is instrumental to study gene function and represents a promising strategy for the treatment of a broad range of diseases. Among ...

IRCCS San Raffaele Institute

Women treated for breast cancer (BC) are at risk of developing secondary tumors, such as lung cancer (LC). Since rare germline variants have been linked to multiple cancer development, we hypothesized that BC survivors might be prone to develop LC as a result of harboring rare variants. Sixty patients with LC with previous BC (the study population; SP) and 53 women with either BC or LC and no secondary cancer (control population; CP) were enrolled. Whole exome sequencing was performed in both tumors and unaffected tissues from 28/60 SP patients, and in germline DNA from 32/53 CP. Candidate genes were validated in the remaining individuals from both populations. We found two main mutational signature profiles: S1 (C>T) in all BCs and 16/28 LCs, and S2 (C>A) which is strongly associated with smoking, in 12/28 LCs. The burden test over rare germline variants in S1-LC vs CP identified 248 genes. Validation confirmed  as significantly associated with LC in never-smokers. In conclusion, our data suggest two signatures involved in LC onset in women with previous BC. One of these signatures is linked to smoking. Conversely, regardless of smoking habit, in a subgroup of BC survivors genetic susceptibility may contribute to LC risk.

Integrated Somatic and Germline Whole-Exome Sequencing Analysis in Women with Lung Cancer after a Previous Breast Cancer.

 Analysis of scATAC-seq data has been recently scaled to thousands of cells. While processing of other types of single cell data was boosted by the implementation of alignment-free techniques, pipelines available to process scATAC-seq data still require large computational resources. We propose here an approach based on pseudoalignment, which reduces the execution times and hardware needs at little cost for precision.  Public data for 10k PBMC were downloaded from 10x Genomics web site. Reads were aligned to various references derived from DNase I Hypersensitive Sites (DHS) using  and quantified with . We compared our results with the ones publicly available derived by . We subsequently tested our approach on scATAC-seq data for K562 cell line.  We found that  does not introduce biases in quantification of known peaks; cells groups identified are consistent with the ones identified from standard method. We also found that cell identification is robust when analysis is performed using DHS-derived reference in place of  identification of ATAC peaks. Lastly, we found that our approach is suitable for reliable quantification of gene activity based on scATAC-seq signal, thus allows for efficient labelling of cell groups based on marker genes.  Analysis of scATAC-seq data by means of  produces results in line with standard pipelines while being considerably faster; using a set of known DHS sites as reference does not affect the ability to characterize the cell populations.

Fast analysis of scATAC-seq data using a predefined set of genomic regions.

Recent efforts have succeeded in surveying open chromatin at the single-cell level, but high-throughput, single-cell assessment of heterochromatin and its underlying genomic determinants remains challenging. We engineered a hybrid transposase including the chromodomain (CD) of the heterochromatin protein-1α (HP-1α), which is involved in heterochromatin assembly and maintenance through its binding to trimethylation of the lysine 9 on histone 3 (H3K9me3), and developed a single-cell method, single-cell genome and epigenome by transposases sequencing (scGET-seq), that, unlike single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq), comprehensively probes both open and closed chromatin and concomitantly records the underlying genomic sequences. We tested scGET-seq in cancer-derived organoids and human-derived xenograft (PDX) models and identified genetic events and plasticity-driven mechanisms contributing to cancer drug resistance. Next, building upon the differential enrichment of closed and open chromatin, we devised a method, Chromatin Velocity, that identifies the trajectories of epigenetic modifications at the single-cell level. Chromatin Velocity uncovered paths of epigenetic reorganization during stem cell reprogramming and identified key transcription factors driving these developmental processes. scGET-seq reveals the dynamics of genomic and epigenetic landscapes underlying any cellular processes.

Chromatin Velocity reveals epigenetic dynamics by single-cell profiling of heterochromatin and euchromatin.

Single cell profiling has been proven to be a powerful tool in molecular biology to understand the complex behaviours of heterogeneous system. The definition of the properties of single cells is the primary endpoint of such analysis, cells are typically clustered to underpin the common determinants that can be used to describe functional properties of the cell mixture under investigation. Several approaches have been proposed to identify cell clusters; while this is matter of active research, one popular approach is based on community detection in neighbourhood graphs by optimisation of modularity. In this paper we propose an alternative and principled solution to this problem, based on Stochastic Block Models. We show that such approach not only is suitable for identification of cell groups, it also provides a solid framework to perform other relevant tasks in single cell analysis, such as label transfer. To encourage the use of Stochastic Block Models, we developed a python library, schist, that is compatible with the popular scanpy framework.

Nested Stochastic Block Models applied to the analysis of single cell data.

scGET-seq simultaneously profiles euchromatin and heterochromatin. scGET-seq exploits the concurrent action of transposase Tn5 and its hybrid form TnH, which targets H3K9me3 domains. Here we present a step-by-step protocol to profile single cells by scGET-seq using a 10× Chromium Controller. We describe steps for transposomes preparation and validation. We detail nuclei preparation and transposition, followed by encapsulation, library preparation, sequencing, and data analysis. For complete details on the use and execution of this protocol, please refer to Tedesco et al. (2022) and de Pretis and Cittaro (2022)..

Article	Year
Integrated Somatic and Germline Whole-Exome Sequencing Analysis in Women with Lung Cancer after a Previous Breast Cancer. Cancers\| PubMed ID: 30925779	2019
Fast analysis of scATAC-seq data using a predefined set of genomic regions. F1000Research\| PubMed ID: 32595951	2020
Chromatin Velocity reveals epigenetic dynamics by single-cell profiling of heterochromatin and euchromatin. Nature biotechnology\| PubMed ID: 34635836	2022
Nested Stochastic Block Models applied to the analysis of single cell data. BMC bioinformatics\| PubMed ID: 34847879	2021
Analyzing genomic and epigenetic profiles in single cells by hybrid transposase (scGET-seq). STAR protocols\| PubMed ID: 37000619	2023

Davide Cittaro

Biography

JoVE Journal Publications

Other Publications