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Understanding of repair outcomes after Cas9-induced DNA cleavage is still limited, especially in primary human cells. We sequence repair outcomes at 1,656 on-target genomic sites in primary human T cells and use these data to train a machine learning model, which we have called CRISPR Repair Outcome (SPROUT). SPROUT accurately predicts the length, probability and sequence of nucleotide insertions and deletions, and will facilitate design of SpCas9 guide RNAs in therapeutically important primary human cells. A machine learning model based on data from primary human T cells accurately predicts repair outcomes after CRISPR–Cas9 editing.

Chromosome conformation capture (3C)-derived methods have become an indispensable tool in the study of gene regulation. The three-dimensional contacts probed by 3C methods depend strongly on the properties of the enzyme used to fragment chromatin prior to proximity-driven ligation. Micrococcal nuclease (MNase), used in Micro-C, increases resolution at the expense of low ligation efficiency and the need for extensive enzyme titration. To overcome these limitations, we engineered a highly active, TEV protease-activatable caspase-activated DNase (CAD) to enable an efficient, low-sequence-bias, and high-resolution proximity ligation assay we call CAD-C. CAD-C was successful on the first attempt for each human cell line tested and the resulting datasets capture loops, TADs, compartments, and stripes similarly to Micro-C. However, compared to Micro-C and Hi-C, CAD-C shows enhanced sensitivity for promoter-enhancer loops. Leveraging the ligation-competent DNA ends produced by CAD cleavage, we show that CAD-C is compatible with a highly streamlined, repair-free protocol and produces multi-step CADwalks, consecutive ligations between nucleosomal or sub-nucleosomal fragments. With these walks, we probe local chromatin fiber folding contacts, nucleosomal and sub-nucleosomal footprints, and long-range nuclear organization regimes in human cell lines. CAD-C is an efficient, robust chromatin structure assay that can span sub-nucleosomal to chromosomal length scales in a single experiment.

CRISPR/Cas technologies have transformed our ability to add functionality to the genome by knock-in of payload via homology-directed repair (HDR). However, a systematic and quantitative profiling of the knock-in integration landscape is still lacking. Here, we present a framework based on long-read sequencing and an integrated computational pipeline (knock-knock) to analyze knock-in repair outcomes across a wide range of experimental parameters. Our data uncover complex repair profiles, with perfect HDR often accounting for a minority of payload integration events, and reveal markedly distinct mis-integration patterns between cell-types or forms of HDR templates used. Our analysis demonstrates that the two sides of a given double-strand break can be repaired by separate pathways and identifies a major role for sequence micro-homology in driving donor mis-integration. Altogether, our comprehensive framework paves the way for investigating repair mechanisms, monitoring accuracy, and optimizing the precision of genome engineering. Footnotes * https://github.com/jeffhussmann/knock-knock

Chromatin compaction by linker histone H1 family proteins is a long-standing model for transcriptional repression. However, the biophysical and conformational details of such compaction , at the kilobase- and sub-kilobase length scale relevant to the activity of transcriptional regulatory elements, remain under debate. Rather than inferring such compaction from indirect measurements of features like DNA accessibility, we sought to directly probe sub-kilobase contacts between nearby nucleosomes. We developed an improved version of radiation-induced correlated cleavage with sequencing (RICC-seq), which we term RICC-seq 2.0, and used it in parallel with Micro-C to cross-validate our measurements of chromatin structure in both diverse cell types with different levels of linker histone and different levels of chromatin compaction, as well as a CRISPRi system for pan-H1 depletion. Using this system, we find that chromatin fiber de-compaction upon H1 depletion is global across the genome, reducing the contrast in inter-nucleosome contacts between acetylated chromatin and the rest of the genome. Surprisingly, this does not dramatically change higher-order chromatin organization such as nuclear compartments. Nevertheless, we observe a broad increase in accessibility at tens of thousands of sites and an increase in expression of over a thousand genes, which are enriched in polycomb repressive complex targets. Investigating the local chromatin compaction at upregulated genes as opposed to genes that do not change transcription, we observe that upregulated genes are not specifically de-compacted. Rather, our data support a model in which linker histone globally induces local compaction of nucleosome contacts and an increase in linker lengths, and repression by PRC1/2 is particularly dependent on these local features of chromatin architecture.

Elucidating the wiring diagram of the human cell is a central goal of the post-genomic era. We combined genome engineering, confocal live-cell imaging, mass spectrometry and data science to systematically map the localization and interactions of human proteins. Our approach provides a data-driven description of the molecular and spatial networks that organize the proteome. Unsupervised clustering of these networks delineates functional communities that facilitate biological discovery, and uncovers that RNA-binding proteins form a specific sub-group defined by unique interaction and localization properties. Furthermore, we discover that remarkably precise functional information can be derived from protein localization patterns, which often contain enough information to identify molecular interactions. Paired with a fully interactive website (opencell.czbiohub.org), we provide a resource for the quantitative cartography of human cellular organization. Competing Interest Statement J.S.W. declares outside interest in Chroma Therapeutics, KSQ Therapeutics, Maze Therapeutics, Amgen, Tessera Therapeutics and 5 AM Ventures. M. M. is an indirect shareholder in EvoSep Biosystems. Footnotes * https://opencell.czbiohub.org/