Automated in situ chromatin profiling efficiently resolves cell types and gene regulatory programs

BackgroundOur understanding of eukaryotic gene regulation is limited by the complexity of protein–DNA interactions that comprise the chromatin landscape and by inefficient methods for characterizing these interactions. We recently introduced CUT&RUN, an antibody-targeted nuclease cleavage method that profiles DNA-binding proteins, histones and chromatin-modifying proteins in situ with exceptional sensitivity and resolution.ResultsHere, we describe an automated CUT&RUN platform and apply it to characterize the chromatin landscapes of human cells. We find that automated CUT&RUN profiles of histone modifications crisply demarcate active and repressed chromatin regions, and we develop a continuous metric to identify cell-type-specific promoter and enhancer activities. We test the ability of automated CUT&RUN to profile frozen tumor samples and find that our method readily distinguishes two pediatric glioma xenografts by their subtype-specific gene expression programs.ConclusionsThe easy, cost-effective workflow makes automated CUT&RUN an attractive tool for high-throughput characterization of cell types and patient samples.

[1]  Lee E. Edsall,et al.  Distinct epigenomic landscapes of pluripotent and lineage-committed human cells. , 2010, Cell stem cell.

[2]  Clifford A. Meyer,et al.  Model-based Analysis of ChIP-Seq (MACS) , 2008, Genome Biology.

[3]  Andrew P Feinberg,et al.  The Key Role of Epigenetics in Human Disease Prevention and Mitigation. , 2018, The New England journal of medicine.

[4]  Tracy T Batchelor,et al.  Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq , 2018, Science.

[5]  Mauro A. A. Castro,et al.  The chromatin accessibility landscape of primary human cancers , 2018, Science.

[6]  K. Czene,et al.  Library Preparation and Multiplex Capture for Massive Parallel Sequencing Applications Made Efficient and Easy , 2012, PloS one.

[7]  Fabian J Theis,et al.  The Human Cell Atlas , 2017, bioRxiv.

[8]  Nathaniel D. Heintzman,et al.  Histone modifications at human enhancers reflect global cell-type-specific gene expression , 2009, Nature.

[9]  Pedro P. Rocha,et al.  CTCF establishes discrete functional chromatin domains at the Hox clusters during differentiation , 2015, Science.

[10]  Daniel J. Gaffney,et al.  AHT-ChIP-seq: a completely automated robotic protocol for high-throughput chromatin immunoprecipitation , 2013, Genome Biology.

[11]  B. Kennedy,et al.  NPAT links cyclin E-Cdk2 to the regulation of replication-dependent histone gene transcription. , 2000, Genes & development.

[12]  Limsoon Wong,et al.  Why Batch Effects Matter in Omics Data, and How to Avoid Them. , 2017, Trends in biotechnology.

[13]  Barbara J. Wold,et al.  Fully automated high-throughput chromatin immunoprecipitation for ChIP-seq: Identifying ChIP-quality p300 monoclonal antibodies , 2014, Scientific Reports.

[14]  S. Henikoff,et al.  An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites , 2016, bioRxiv.

[15]  B. Göttgens Regulatory network control of blood stem cells. , 2015, Blood.

[16]  Nathaniel D. Heintzman,et al.  Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome , 2007, Nature Genetics.

[17]  Marc D. Perry,et al.  ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia , 2012, Genome research.

[18]  Data production leads,et al.  An integrated encyclopedia of DNA elements in the human genome , 2012 .

[19]  A. Shilatifard,et al.  Epigenetics of hematopoiesis and hematological malignancies , 2016, Genes & development.

[20]  Howard Y. Chang,et al.  Genome-Wide Temporal Profiling of Transcriptome and Open Chromatin of Early Cardiomyocyte Differentiation Derived From hiPSCs and hESCs , 2017, Circulation research.

[21]  Kun Mu,et al.  Integrated Molecular Meta-Analysis of 1,000 Pediatric High-Grade and Diffuse Intrinsic Pontine Glioma , 2017, Cancer cell.

[22]  S. Nicosia,et al.  One mouse, one patient paradigm: New avatars of personalized cancer therapy. , 2014, Cancer letters.

[23]  Michael J. T. Stubbington,et al.  The Human Cell Atlas: from vision to reality , 2017, Nature.

[24]  J. Massagué,et al.  Controlling TGF-β signaling , 2000, Genes & Development.

[25]  Graziano Martello,et al.  The nature of embryonic stem cells. , 2014, Annual review of cell and developmental biology.

[26]  T. Hughes,et al.  The Human Transcription Factors , 2018, Cell.

[27]  Ryan A. Flynn,et al.  A unique chromatin signature uncovers early developmental enhancers in humans , 2011, Nature.

[28]  Galt P. Barber,et al.  BigWig and BigBed: enabling browsing of large distributed datasets , 2010, Bioinform..

[29]  Alexander van Oudenaarden,et al.  Highly expressed loci are vulnerable to misleading ChIP localization of multiple unrelated proteins , 2013, Proceedings of the National Academy of Sciences.

[30]  David M. Simcha,et al.  Tackling the widespread and critical impact of batch effects in high-throughput data , 2010, Nature Reviews Genetics.

[31]  Meng Li,et al.  Transcriptional Dependencies in Diffuse Intrinsic Pontine Glioma. , 2017, Cancer cell.

[32]  David T. W. Jones,et al.  Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma , 2012, Nature.

[33]  Stephan J Sanders,et al.  The autism-associated chromatin modifier CHD8 regulates other autism risk genes during human neurodevelopment , 2015, Nature Communications.

[34]  M. Ramalho-Santos,et al.  Open chromatin in pluripotency and reprogramming , 2010, Nature Reviews Molecular Cell Biology.

[35]  Fidel Ramírez,et al.  deepTools2: a next generation web server for deep-sequencing data analysis , 2016, Nucleic Acids Res..

[36]  Aaron R. Quinlan,et al.  BIOINFORMATICS APPLICATIONS NOTE , 2022 .

[37]  Tobias Straub,et al.  Active promoters give rise to false positive ‘Phantom Peaks’ in ChIP-seq experiments , 2015, Nucleic acids research.

[38]  Martha L. Bulyk,et al.  Direct Promoter Repression by BCL11A Controls the Fetal to Adult Hemoglobin Switch , 2018, Cell.

[39]  T. Meehan,et al.  An atlas of active enhancers across human cell types and tissues , 2014, Nature.

[40]  James A. Cuff,et al.  A Bivalent Chromatin Structure Marks Key Developmental Genes in Embryonic Stem Cells , 2006, Cell.

[41]  C. Glass,et al.  The selection and function of cell type-specific enhancers , 2015, Nature Reviews Molecular Cell Biology.

[42]  R. Young,et al.  Histone H3K27ac separates active from poised enhancers and predicts developmental state , 2010, Proceedings of the National Academy of Sciences.

[43]  Robert Tjian,et al.  Looping Back to Leap Forward: Transcription Enters a New Era , 2014, Cell.

[44]  O. Rando,et al.  Profiling of pluripotency factors in individual stem cells and early embryos , 2018, bioRxiv.

[45]  Julia A. Lasserre,et al.  Histone modification levels are predictive for gene expression , 2010, Proceedings of the National Academy of Sciences.

[46]  J. Wysocka,et al.  Modification of enhancer chromatin: what, how, and why? , 2013, Molecular cell.

[47]  Steven L Salzberg,et al.  Fast gapped-read alignment with Bowtie 2 , 2012, Nature Methods.

[48]  J. Massagué,et al.  Controlling TGF-beta signaling. , 2000, Genes & development.

[49]  S. Henikoff,et al.  Targeted in situ genome-wide profiling with high efficiency for low cell numbers , 2018, Nature Protocols.

[50]  ENCODEConsortium,et al.  An Integrated Encyclopedia of DNA Elements in the Human Genome , 2012, Nature.

[51]  Helena Nord,et al.  lobChIP: from cells to sequencing ready ChIP libraries in a single day , 2015, Epigenetics & Chromatin.

[52]  Vishwanath R. Iyer,et al.  Widespread Misinterpretable ChIP-seq Bias in Yeast , 2013, PloS one.