Lhx2 regulates temporal changes in chromatin accessibility and transcription factor binding in retinal progenitor cells

Zibetti, Cristina; Liu, Sheng; Wan, Jun; Qian, Jiang; Blackshaw, Seth

doi:10.1101/238279

Cited by 3 publications

(6 citation statements)

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“…Notwithstanding, the spurious contribution of heterogeneous cell types from whole-cell populations can be circumvented by several purification strategies prior to bulk sequencing, such as immunolabeling, flow sorting [ 75 , 425 ], or through in vivo biotin labeling of the nuclear envelope, followed by affinity purification of tagged nuclei (INTACT) [ 426 ], leading to a reasonably pure cell population by virtue of a reporter. Conversely, rarefaction issues are known to occur in single-cell sequencing due to the sparsity of the signal generated by few cells, the noise, and the underrepresentation of rare amplicons, which is hard to obviate.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Deciphering the Retinal Epigenome during Development, Disease and Reprogramming: Advancements, Challenges and Perspectives

Zibetti

2022

Cells

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Retinal neurogenesis is driven by concerted actions of transcription factors, some of which are expressed in a continuum and across several cell subtypes throughout development. While seemingly redundant, many factors diversify their regulatory outcome on gene expression, by coordinating variations in chromatin landscapes to drive divergent retinal specification programs. Recent studies have furthered the understanding of the epigenetic contribution to the progression of age-related macular degeneration, a leading cause of blindness in the elderly. The knowledge of the epigenomic mechanisms that control the acquisition and stabilization of retinal cell fates and are evoked upon damage, holds the potential for the treatment of retinal degeneration. Herein, this review presents the state-of-the-art approaches to investigate the retinal epigenome during development, disease, and reprogramming. A pipeline is then reviewed to functionally interrogate the epigenetic and transcriptional networks underlying cell fate specification, relying on a truly unbiased screening of open chromatin states. The related work proposes an inferential model to identify gene regulatory networks, features the first footprinting analysis and the first tentative, systematic query of candidate pioneer factors in the retina ever conducted in any model organism, leading to the identification of previously uncharacterized master regulators of retinal cell identity, such as the nuclear factor I, NFI. This pipeline is virtually applicable to the study of genetic programs and candidate pioneer factors in any developmental context. Finally, challenges and limitations intrinsic to the current next-generation sequencing techniques are discussed, as well as recent advances in super-resolution imaging, enabling spatio-temporal resolution of the genome.

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Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Deciphering the Retinal Epigenome during Development, Disease and Reprogramming: Advancements, Challenges and Perspectives

Zibetti

2022

Cells

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“…FACS-sorted Chx10:GFP+ RPCs and Chx10:GFPpost-mitotic retinal neurons (Rowan and Cepko, 2004) were collected from the developing mouse retina at three time points, Embryonic day 14 (E14), Embryonic day (E18), Postnatal day 2 (P2), and subjected to standard bulk RNA sequencing (Zibetti et al, 2017). We applied our previous genome wide GWCoGAPS pipeline for bulk RNA-Seq to the normalized FPKM gene expression estimates to identify a latent space consisting of 10 patterns of coregulated genes (Stein-O'Brien et al, 2017b).…”

Section: Assessing Latent Spaces and Dimensionality: Lessons From Bulmentioning

confidence: 99%

“…Chromatin derived from flow-sorted Chx10:Cre-GFPpositive (Rowan and Cepko, 2004) retinal fractions was processed as previously described (Zibetti et al, 2017). Briefly, chromatin was extracted and processed for Tn5 mediated tagmentation and adapter incorporation, according to the Manufacturer's protocol (Nextera DNA sample preparation kit, Illumina) at 37°C for 30 min.…”

Section: Atac-seq Of the Developing Mouse Retinamentioning

confidence: 99%

Decomposing cell identity for transfer learning across cellular measurements, platforms, tissues, and species

Stein-O’Brien

Clark

Sherman

et al. 2018

Preprint

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New approaches are urgently needed to glean biological insights from the vast amounts of single cell RNA sequencing (scRNA-Seq) data now being generated. To this end, we propose that cell identity should map to a reduced set of factors which will describe both exclusive and shared biology of individual cells, and that the dimensions which contain these factors reflect biologically meaningful relationships across different platforms, tissues and species. To find a robust set of dependent factors in large-scale scRNA-Seq data, we developed a Bayesian non-negative matrix factorization (NMF) algorithm, scCoGAPS. Application of scCoGAPS to scRNA-Seq data obtained over the course of mouse retinal development identified * These authors contributed equally gene expression signatures for factors associated with specific cell types and continuous biological processes. To test whether these signatures are shared across diverse cellular contexts, we developed projectR to map biologically disparate datasets into the factors learned by scCoGAPS. Because projecting these dimensions preserve relative distances between samples, biologically meaningful relationships/factors will stratify new data consistent with their underlying processes, allowing labels or information from one dataset to be used for annotation of the other-a machine learning concept called transfer learning. Using projectR, data from multiple datasets was used to annotate latent spaces and reveal novel parallels between developmental programs in other tissues, species and cellular assays. Using this approach we are able to transfer cell type and state designations across datasets to rapidly annotate cellular features in a new dataset without a priori knowledge of their type, identify a species-specific signature of microglial cells, and identify a previously undescribed subpopulation of neurosecretory cells within the lung. Together, these algorithms define biologically meaningful dimensions of cellular identity, state, and trajectories that persist across technologies, molecular features, and species. Genes Patterns Meta b o li c Sta te C e ll T ype Dise a s e Stat e

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“…FACS-sorted Chx10:GFP+ RPCs and Chx10:GFPpost-mitotic retinal neurons (Rowan and Cepko, 2004) were collected from the developing mouse retina at three time points, Embryonic day 14 (E14), Embryonic day (E18), Postnatal day 2 (P2), and subjected to standard bulk RNA sequencing (Zibetti et al, 2017). We applied our previous genome wide GWCoGAPS pipeline for bulk RNA-Seq to the normalized FPKM gene expression estimates to identify a latent space consisting of 10 patterns of co-regulated genes (Stein-O'Brien et al, 2017).…”

Section: Applications Assessing Latent Spaces and Dimensionality: Lesmentioning

confidence: 99%

“…Chromatin derived from flow-sorted Chx10:Cre-GFP + (Rowan and Cepko, 2004) retinal fractions was processed as previously described (Zibetti et al, 2017). Briefly, chromatin was extracted and processed for Tn5 mediated tagmentation and adapter incorporation, according to the Manufacturer's protocol (Nextera DNA sample preparation kit, Illumina) at 37°C for 30 min.…”

Section: Atac-seq Of the Developing Mouse Retina Obtained From Zibettmentioning

confidence: 99%

Faculty Opinions recommendation of Decomposing Cell Identity for Transfer Learning across Cellular Measurements, Platforms, Tissues, and Species.

Guigó

2019

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature

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Author Contributions: GSO'B, BSC, SB, LAG and EJF conceived and directed the study. BSC generated scRNA-Seq data. GSO'B, BSC, LAG and EJF analyzed scRNA-Seq data, with LAG and EJF as senior bioinformaticians. CZ and BSC generated the bulk RNA-Seq, and CZ generated the ATAC-Seq data. GSO'B, TS, LAG and EJF developed scCoGAPS. GSO'B, RS, CC, LAG, and EJF contributed to the development of projectR. QH and GSO'B developed the random forest classifier for the projections of bulk GWCoGAPS patterns. RS and GSO'B developed the AUC evaluation method for projected pattern weights included in the projectR package. SL, CZ, JQ, and GSO'B analyzed the ATAC-Seq data.

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Lhx2 regulates temporal changes in chromatin accessibility and transcription factor binding in retinal progenitor cells

Abstract: (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

Cited by 3 publications

References 39 publications

Deciphering the Retinal Epigenome during Development, Disease and Reprogramming: Advancements, Challenges and Perspectives

Deciphering the Retinal Epigenome during Development, Disease and Reprogramming: Advancements, Challenges and Perspectives

Decomposing cell identity for transfer learning across cellular measurements, platforms, tissues, and species

Faculty Opinions recommendation of Decomposing Cell Identity for Transfer Learning across Cellular Measurements, Platforms, Tissues, and Species.

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