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Author

Marta Grzelak

Other affiliations: University of Warsaw
Bio: Marta Grzelak is an academic researcher from University of Cambridge. The author has contributed to research in topics: Progenitor cell & Stem cell. The author has an hindex of 9, co-authored 12 publications receiving 741 citations. Previous affiliations of Marta Grzelak include University of Warsaw.

Papers
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Journal ArticleDOI
TL;DR: Advances in RNA-sequencing technologies and methods over the past decade are discussed and adaptations that are enabling a fuller understanding of RNA biology are outlined, from when and where an RNA is expressed to the structures it adopts.
Abstract: Over the past decade, RNA sequencing (RNA-seq) has become an indispensable tool for transcriptome-wide analysis of differential gene expression and differential splicing of mRNAs. However, as next-generation sequencing technologies have developed, so too has RNA-seq. Now, RNA-seq methods are available for studying many different aspects of RNA biology, including single-cell gene expression, translation (the translatome) and RNA structure (the structurome). Exciting new applications are being explored, such as spatial transcriptomics (spatialomics). Together with new long-read and direct RNA-seq technologies and better computational tools for data analysis, innovations in RNA-seq are contributing to a fuller understanding of RNA biology, from questions such as when and where transcription occurs to the folding and intermolecular interactions that govern RNA function.

947 citations

Journal ArticleDOI
TL;DR: The use of single-cell RNA sequencing is used to determine the gene expression profile of MECs across four developmental stages; nulliparous, mid gestation, lactation and post involution, providing a global, unbiased view of adult mammary gland development.
Abstract: Characterising the hierarchy of mammary epithelial cells (MECs) and how they are regulated during adult development is important for understanding how breast cancer arises. Here we report the use of single-cell RNA sequencing to determine the gene expression profile of MECs across four developmental stages; nulliparous, mid gestation, lactation and post involution. Our analysis of 23,184 cells identifies 15 clusters, few of which could be fully characterised by a single marker gene. We argue instead that the epithelial cells-especially in the luminal compartment-should rather be conceptualised as being part of a continuous spectrum of differentiation. Furthermore, our data support the existence of a common luminal progenitor cell giving rise to intermediate, restricted alveolar and hormone-sensing progenitors. This luminal progenitor compartment undergoes transcriptional changes in response to a full pregnancy, lactation and involution. In summary, our results provide a global, unbiased view of adult mammary gland development.

217 citations

Journal ArticleDOI
31 Oct 2018
TL;DR: i-BLESS has three key advantages: it is the only unbiased method applicable to yeast, achieves a sensitivity of one break at a given position in 100,000 cells, and eliminates background noise while still allowing for fixation of samples.
Abstract: Maintenance of genome stability is a key issue for cell fate that could be compromised by chromosome deletions and translocations caused by DNA double-strand breaks (DSBs). Thus development of precise and sensitive tools for DSBs labeling is of great importance for understanding mechanisms of DSB formation, their sensing and repair. Until now there has been no high resolution and specific DSB detection technique that would be applicable to any cells regardless of their size. Here, we present i-BLESS, a universal method for direct genome-wide DNA double-strand break labeling in cells immobilized in agarose beads. i-BLESS has three key advantages: it is the only unbiased method applicable to yeast, achieves a sensitivity of one break at a given position in 100,000 cells, and eliminates background noise while still allowing for fixation of samples. The method allows detection of ultra-rare breaks such as those forming spontaneously at G-quadruplexes.

39 citations

Journal ArticleDOI
TL;DR: In this article, the tumor-derived DNA fraction in cerebrospinal fluid (CSF), plasma, and urine samples from patients using sequencing of personalized capture panels guided by analysis of matched tumor biopsies was determined.
Abstract: Glioma-derived cell-free DNA (cfDNA) is challenging to detect using liquid biopsy because quantities in body fluids are low. We determined the glioma-derived DNA fraction in cerebrospinal fluid (CSF), plasma, and urine samples from patients using sequencing of personalized capture panels guided by analysis of matched tumor biopsies. By sequencing cfDNA across thousands of mutations, identified individually in each patient's tumor, we detected tumor-derived DNA in the majority of CSF (7/8), plasma (10/12), and urine samples (10/16), with a median tumor fraction of 6.4 × 10-3 , 3.1 × 10-5 , and 4.7 × 10-5 , respectively. We identified a shift in the size distribution of tumor-derived cfDNA fragments in these body fluids. We further analyzed cfDNA fragment sizes using whole-genome sequencing, in urine samples from 35 glioma patients, 27 individuals with non-malignant brain disorders, and 26 healthy individuals. cfDNA in urine of glioma patients was significantly more fragmented compared to urine from patients with non-malignant brain disorders (P = 1.7 × 10-2 ) and healthy individuals (P = 5.2 × 10-9 ). Machine learning models integrating fragment length could differentiate urine samples from glioma patients (AUC = 0.80-0.91) suggesting possibilities for truly non-invasive cancer detection.

38 citations

Journal ArticleDOI
TL;DR: A fast, flexible and cost-effective method to profile multiple genes simultaneously in low input cell-free DNA (cfDNA): Next Generation-Targeted Amplicon Sequencing (NG-TAS).
Abstract: Circulating tumour DNA (ctDNA) detection and monitoring have enormous potential clinical utility in oncology. We describe here a fast, flexible and cost-effective method to profile multiple genes simultaneously in low input cell-free DNA (cfDNA): Next Generation-Targeted Amplicon Sequencing (NG-TAS). We designed a panel of 377 amplicons spanning 20 cancer genes and tested the NG-TAS pipeline using cell-free DNA from two HapMap lymphoblastoid cell lines. NG-TAS consistently detected mutations in cfDNA when mutation allele fraction was > 1%. We applied NG-TAS to a clinical cohort of metastatic breast cancer patients, demonstrating its potential in monitoring the disease. The computational pipeline is available at https://github.com/cclab-brca/NGTAS_pipeline .

27 citations


Cited by
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Journal ArticleDOI
TL;DR: SoupX, a tool for removing ambient RNA contamination from droplet-based single-cell RNA sequencing experiments, has broad applicability, and its application can improve the biological utility of existing and future datasets.
Abstract: Background Droplet-based single-cell RNA sequence analyses assume that all acquired RNAs are endogenous to cells. However, any cell-free RNAs contained within the input solution are also captured by these assays. This sequencing of cell-free RNA constitutes a background contamination that confounds the biological interpretation of single-cell transcriptomic data. Results We demonstrate that contamination from this "soup" of cell-free RNAs is ubiquitous, with experiment-specific variations in composition and magnitude. We present a method, SoupX, for quantifying the extent of the contamination and estimating "background-corrected" cell expression profiles that seamlessly integrate with existing downstream analysis tools. Applying this method to several datasets using multiple droplet sequencing technologies, we demonstrate that its application improves biological interpretation of otherwise misleading data, as well as improving quality control metrics. Conclusions We present SoupX, a tool for removing ambient RNA contamination from droplet-based single-cell RNA sequencing experiments. This tool has broad applicability, and its application can improve the biological utility of existing and future datasets.

373 citations

Journal ArticleDOI
TL;DR: How large-scale comparative studies are characterizing the degree to which mRNA and protein levels correlate is discussed, and how transcriptomics and proteomics provide useful non-redundant readouts of gene expression is described.
Abstract: Gene expression involves transcription, translation and the turnover of mRNAs and proteins. The degree to which protein abundances scale with mRNA levels and the implications in cases where this dependency breaks down remain an intensely debated topic. Here we review recent mRNA-protein correlation studies in the light of the quantitative parameters of the gene expression pathway, contextual confounders and buffering mechanisms. Although protein and mRNA levels typically show reasonable correlation, we describe how transcriptomics and proteomics provide useful non-redundant readouts. Integrating both types of data can reveal exciting biology and is an essential step in refining our understanding of the principles of gene expression control.

332 citations

Journal ArticleDOI
TL;DR: Recent advancements are explored and the current gaps in knowledge concerning each point of contact between cfDNA analysis and the different stages of cancer management are highlighted.

329 citations

Posted ContentDOI
20 Apr 2018-bioRxiv
TL;DR: This work presents a method, SoupX, for quantifying the extent of the contamination and estimating “background corrected”, cell expression profiles that can be integrated with existing downstream analysis tools and shows that the application of this method reduces batch effects, strengthens cell-specific quality control and improves biological interpretation.
Abstract: Droplet based single cell RNA sequence analyses assume all acquired RNAs are endogenous to cells. However, any cell free RNAs contained within the input solution are also captured by these assays. This sequencing of cell free RNA constitutes a background contamination that has the potential to confound the correct biological interpretation of single cell transcriptomic data. Here, we demonstrate that contamination from this "soup" of cell free RNAs is ubiquitous, experiment specific in its composition and magnitude, and can lead to erroneous biological conclusions. We present a method, SoupX, for quantifying the extent of the contamination and estimating "background corrected", cell expression profiles that can be integrated with existing downstream analysis tools. We apply this method to two data-sets and show that the application of this method reduces batch effects, strengthens cell-specific quality control and improves biological interpretation.

327 citations

Journal ArticleDOI
12 Oct 2017-Blood
TL;DR: Recent advances and unanswered questions at the intersection between inflammation and HSC biology in the contexts of development, aging, and hematological malignancy are detailed.

268 citations