Single-cell high-throughput chromatin conformation capture methodologies (scHi-C) enable profiling of long-range genomic interactions. However, data from these technologies are prone to technical noise and biases that hinder downstream analysis. We develop a normalization approach, BandNorm, and a deep generative modeling framework, scVI-3D, to account for scHi-C specific biases. In benchmarking experiments, BandNorm yields leading performances in a time and memory efficient manner for cell-type separation, identification of interacting loci, and recovery of cell-type relationships, while scVI-3D exhibits advantages for rare cell types and under high sparsity scenarios. Application of BandNorm coupled with gene-associating domain analysis reveals scRNA-seq validated sub-cell type identification.

The development and function of stem and progenitor cells that produce blood cells are vital in physiology. GATA2 mutations cause GATA-2-deficiency syndrome involving immunodeficiency, myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). GATA-2 physiological activities necessitate that it be strictly regulated, and cell type-specific enhancers fulfill this role. The +9.5 intronic enhancer harbors multiple conserved cis-elements, and germline mutations of these cis-elements are pathogenic in humans. Since mechanisms underlying how GATA2 enhancer disease mutations impact hematopoiesis and pathology are unclear, we generated mouse models of the enhancer mutations. While a multi-motif mutant was embryonic lethal, a single-nucleotide Ets motif mutant was viable, and steady-state hematopoiesis was normal. However, the Ets motif mutation abrogated stem/progenitor cell regeneration following stress. These results reveal a new mechanism in human genetics in which a disease predisposition mutation inactivates enhancer regenerative activity, while sparing developmental activity. Mutational sensitization to stress that instigates hematopoietic failure constitutes a paradigm for GATA-2-deficiency syndrome and other contexts of GATA-2-dependent pathogenesis.

Genome-wide hypertranscription is common in human cancer and predicts poor prognosis. To understand how hypertranscription might drive cancer, we applied our formalin-fixed paraffin-embedded (FFPE)–cleavage under targeted accessible chromatin method for mapping RNA polymerase II (RNAPII) genome-wide in FFPE sections. We demonstrate global RNAPII elevations in mouse gliomas and assorted human tumors in small clinical samples and discover regional elevations corresponding to de novo HER2 amplifications punctuated by likely selective sweeps. RNAPII occupancy at S-phase-dependent histone genes correlated with WHO grade in meningiomas, accurately predicted rapid recurrence, and corresponded to whole-arm chromosome losses. Elevated RNAPII at histone genes in meningiomas and diverse breast cancers is consistent with histone production being rate-limiting for S-phase progression and histone gene hypertranscription driving overproliferation and aneuploidy in cancer, with general implications for precision oncology.

Current Hi-C analysis approaches are unable to account for reads that align to multiple locations, and hence underestimate biological signal from repetitive regions of genomes. We developed and validated mHi-C, a multi-read mapping strategy to probabilistically allocate Hi-C multi-reads. mHi-C exhibited superior performance over utilizing only uni-reads and heuristic approaches aimed at rescuing multi-reads on benchmarks. Specifically, mHi-C increased the sequencing depth by an average of 20% resulting in higher reproducibility of contact matrices and detected interactions across biological replicates. The impact of the multi-reads on the detection of significant interactions is influenced marginally by the relative contribution of multi-reads to the sequencing depth compared to uni-reads, cis-to-trans ratio of contacts, and the broad data quality as reflected by the proportion of mappable reads of datasets. Computational experiments highlighted that in Hi-C studies with short read lengths, mHi-C rescued multi-reads can emulate the effect of longer reads. mHi-C also revealed biologically supported bona fide promoter-enhancer interactions and topologically associating domains involving repetitive genomic regions, thereby unlocking a previously masked portion of the genome for conformation capture studies.

This tutorial is designed for processing and analyzing CUT&Tag data following the Benchtop CUT&Tag V.3 protocol. The illustration data used in this tutorial is the profiling of histone modifications in the human lymphoma K562 cell line, but the tutorial is generally applicable to any chromatin protein, including transcription factors, RNA polymerase II, and epitope-tagged proteins. For reproducible analysis, this tutorial is also available on GitHub at https://yezhengstat.github.io/CUTTag_tutorial/.