A parallelized, automated platform enabling individual or sequential ChIP of histone marks and transcription factors.

Despite its popularity, chromatin immunoprecipitation followed by sequencing (ChIP-seq) remains a tedious (>2 d), manually intensive, low-sensitivity and low-throughput approach. Here, we combine principles of microengineering, surface chemistry, and molecular biology to address the major limitations of standard ChIP-seq. The resulting technology, FloChIP, automates and miniaturizes ChIP in a beadless fashion while facilitating the downstream library preparation process through on-chip chromatin tagmentation. FloChIP is fast (<2 h), has a wide dynamic range (from 106 to 500 cells), is scalable and parallelized, and supports antibody- or sample-multiplexed ChIP on both histone marks and transcription factors. In addition, FloChIP's interconnected design allows for straightforward chromatin reimmunoprecipitation, which allows this technology to also act as a microfluidic sequential ChIP-seq system. Finally, we ran FloChIP for the transcription factor MEF2A in 32 distinct human lymphoblastoid cell lines, providing insights into the main factors driving collaborative DNA binding of MEF2A and into its role in B cell-specific gene regulation. Together, our results validate FloChIP as a flexible and reproducible automated solution for individual or sequential ChIP-seq.

MOWChIP-seq for low-input and multiplexed profiling of genome-wide histone modifications.

Epigenetic mechanisms such as histone modifications play critical roles in adaptive tuning of chromatin structures. Profiling of various histone modifications at the genome scale using tissues from animal and human samples is an important step for functional studies of epigenomes and epigenomics-based precision medicine. Because the profile of a histone mark is highly specific to a cell type, cell isolation from tissues is often necessary to generate a homogeneous cell population, and such operations tend to yield a low number of cells. In addition, high-throughput processing is often desirable because of the multiplexity of histone marks of interest and the large quantity of samples in a hospital setting. In this protocol, we provide detailed instructions for device fabrication, setup, and operation of microfluidic oscillatory washing-based chromatin immunoprecipitation followed by sequencing (MOWChIP-seq) for profiling of histone modifications using as few as 100 cells per assay with a throughput as high as eight assays in one run. MOWChIP-seq operation involves flowing of chromatin fragments through a packed bed of antibody-coated beads, followed by vigorous microfluidic oscillatory washing. Our process is semi-automated to reduce labor and improve reproducibility. Using one eight-unit device, it takes 2 d to produce eight sequencing libraries from chromatin samples. The technology is scalable. We used the protocol to study a number of histone modifications in various types of mouse and human tissues. The protocol can be conducted by a user who is familiar with molecular biology procedures and has basic engineering skills.

SCALE method for single-cell ATAC-seq analysis via latent feature extraction.

Single-cell ATAC-seq (scATAC-seq) profiles the chromatin accessibility landscape at single cell level, thus revealing cell-to-cell variability in gene regulation. However, the high dimensionality and sparsity of scATAC-seq data often complicate the analysis. Here, we introduce a method for analyzing scATAC-seq data, called Single-Cell ATAC-seq analysis via Latent feature Extraction (SCALE). SCALE combines a deep generative framework and a probabilistic Gaussian Mixture Model to learn latent features that accurately characterize scATAC-seq data. We validate SCALE on datasets generated on different platforms with different protocols, and having different overall data qualities. SCALE substantially outperforms the other tools in all aspects of scATAC-seq data analysis, including visualization, clustering, and denoising and imputation. Importantly, SCALE also generates interpretable features that directly link to cell populations, and can potentially reveal batch effects in scATAC-seq experiments.