New technologies for high-throughput biology

Important biological discoveries are often enabled by groundbreaking new technologies. We are strongly committed to technology development, especially in the areas of epigenome profiling, single-cell sequencing, genome/epigenome editing, and synthetic biology.

Epigenome profiling. We have developed a series of methods for low-input and single-cell epigenome profiling, which includes ChIPmentation for histone marks and single-cell whole genome bisulfite sequencing (scWGBS) for DNA methylation. Moreover, we have established robust end-to-end pipelines for DNA methylation profiling (using an optimized RRBS protocol) and for chromatin accessibility profiling (using an optimized ATAC-seq protocol), with which we have processed several thousand patient samples each, in numerous projects and collaborations.

Single-cell sequencing. Our lab has been an early adopter of various single-cell sequencing technologies including Smart-seq, Drop-seq, and 10x Genomics profiling. Moreover, we have recently developed the scifi-RNA-seq method, which combines combinatorial indexing with droplet microfluidics for single-cell RNA-seq in millions of cells and thousands of samples.

CRISPR screening. We have developed the CROP-seq technology for CRISPR screening with single-cell transcriptome readout. This method enables functional biology at scale, with the potential of accelerating biological discovery and clinical translation. CROP-seq combines the scalability of pooled CRISPR screening with single-cell transcriptome sequencing as its readout, making it possible to screen for complex regulatory phenotypes.

Collectively, these methods – and additional new methods that are currently under development in our lab – enable us to study biological processes in high resolution and high throughput, in order to home in on disease-relevant mechanisms.

Publications

Single-cell RNA-seq with spike-in cells enables accurate quantification of cell-specific drug effects in pancreatic islets

Single-cell RNA-seq with spike-in cells enables accurate quantification of cell-specific drug effects in pancreatic islets

Marquina-Sanchez B*, Fortelny N*, Farlik M*, Vieira A, Collombat P, Bock C*, Kubicek S*

Genome Biology  21, 106 (2020). DOI: 10.1186/s13059-020-02006-2

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Ultra-high throughput single-cell RNA sequencing by combinatorial fluidic indexing

Ultra-high throughput single-cell RNA sequencing by combinatorial fluidic indexing

Datlinger P*, Rendeiro AF*, Boenke T, Krausgruber T, Barreca D, Bock C

Manuscript submitted  (2019). DOI: 10.1101/2019.12.17.879304

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Pooled CRISPR screening with single-cell transcriptome readout

Pooled CRISPR screening with single-cell transcriptome readout

Datlinger P, Schmidl C, Rendeiro A, Krausgruber T, Traxler P, Klughammer J, Schuster L, Kuchler A, Alpar D, Bock C

Nature Methods  14, 297-301 (2017). DOI: 10.1038/nmeth.4177

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Quantitative comparison of DNA methylation assays for large-scale validation and epigenetic biomarker development

Quantitative comparison of DNA methylation assays for large-scale validation and epigenetic biomarker development

Bock C, Halbritter F, Carmona FJ, Tierling S, Datlinger P, Assenov Y, Berdasco M, Bergmann AK, Booher K, Busato F, Campan M, Dahl C, Dahmcke CM, Diep D, Fernández AF, Gerhauser C, Haake A, Heilmann K, Holcomb T, Hussmann D, Ito M, Kreutz M, Kulis M, Lopez V, Nair SS, Paul DS, Plongthongkum N, Qu W, Queirós AC, Sauter G, Schlomm T, Stirzaker C, Statham A, Strogantsev R, Urdinguio RG, Walter K, Weichenhan D, Weisenberger DJ, Beck S, Clark SJ, Esteller M, Ferguson-Smith AC, Fraga MF, Guldberg P, Hansen LL, Laird PW, Martin-Subero JI, Nygren AOH, Peist R, Plass C, Shames DS, Siebert R, Sun X, Tost J, Walter J, Zhang K, for the BLUEPRINT consortium

Nature Biotechnology  34, 726-737 (2016). DOI: 10.1038/nbt.3605

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Multi-omics of single cells: Strategies and applications

Multi-omics of single cells: Strategies and applications

Bock C, Farlik M, Sheffield NC

Trends in Biotechnology  34, 605-608 (2016). DOI: 10.1016/j.tibtech.2016.04.004

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Differential DNA methylation analysis without a reference genome

Differential DNA methylation analysis without a reference genome

Klughammer J, Datlinger P, Printz D, Sheffield NC, Farlik M, Hadler J, Fritsch G, Bock C

Cell Reports  13, 2621-2633 (2015). DOI: 10.1016/j.celrep.2015.11.024

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ChIPmentation: fast, robust, low-input ChIP-seq for histones and transcription factors

ChIPmentation: fast, robust, low-input ChIP-seq for histones and transcription factors

Schmidl C*, Rendeiro AF*, Sheffield NC, Bock C

Nature Methods  12, 963-965 (2015). DOI: 10.1038/nmeth.3542

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Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics

Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics

Farlik M*, Sheffield NC*, Nuzzo A, Datlinger P, Schönegger A, Klughammer J, Bock C

Cell Reports  10, 1386-1397 (2015). DOI: 10.1016/j.celrep.2015.02.001

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Analysing and interpreting DNA methylation data

Analysing and interpreting DNA methylation data

Bock C

Nature Reviews Genetics  13, 705-719 (2012). DOI: 10.1038/nrg3273

* shared first or shared senior authorship

Differential DNA methylation analysis without a reference genome