Yanxiang Deng, Ph.D.

  1. Spatially resolved profiling of histone modifications

To investigate the mechanisms underlying spatial organization of different cell types and functions in the tissue context, it is highly desired to examine not only gene expression but also epigenetic underpinnings in a spatially resolved manner to uncover the causative relationship determining what drives tissue organization and function. Despite recent advances in spatial transcriptomics to map gene expression, it has not been possible to determine the underlying epigenetic mechanisms controlling gene expression and tissue development with high spatial resolution. We developed a first-of-its-kind technology called spatial-CUT&Tag for genome-wide profiling of histone modifications pixel by pixel on a frozen tissue section without dissociation. This method resolved spatially distinct and cell-type-specific chromatin modifications in mouse embryonic organogenesis and postnatal brain development. Single-cell epigenomic profiles were derived from the tissue pixels containing single nuclei. Spatial-CUT&Tag adds a new dimension to spatial biology by enabling the mapping of epigenetic regulations broadly implicated in development and disease. In addition, epigenetic drugs are emerging now, so potentially we can develop drugs to target those epigenetic mechanisms. Having the tools to understand the epigenetic origin of different disease states could open up a whole new avenue of therapeutics.

  • Deng, Y., Bartosovic, M., Kukanja, P., Zhang, D., Liu, Y., Su, G., Enninful, A., Bai, Z., Castelo-Branco, G. and Fan, R. Spatial-CUT&Tag: spatially resolved chromatin modification profiling at the cellular level, Science, 375: 681-686 (2022).
  1. Spatially resolved profiling of chromatin accessibility

An ambitious global initiative has been undertaken to map cell types across all human organs. Single-cell sequencing has been critical to this effort, but it is hard to map the location of cell types to the original tissue environment. We developed spatial-ATAC-seq, which for the first time allows for directly observing cell types in a tissue as defined by global epigenetic state. Spatial-ATAC-seq allows us to identify which regions of the chromatin are accessible genome-wide in cells at specific locations in a tissue. This chromatin accessibility is required for genes to be activated, which then provides unique insights on the molecular status of any given cell. Combining the ability to analyze chromatin accessibility with the spatial location of cells is a breakthrough that can improve our understanding of cell identity, cell state and the underlying mechanisms that determine the expression of genes in the development of different tissues or diseases. Profiling mouse embryos using spatial-ATAC-seq delineated tissue-region-specific epigenetic landscapes and identified gene regulators involved in the development of the central nervous system. Mapping the accessible genome in the mouse and human brain revealed the intricate arealization of brain regions. Applying spatial-ATAC-seq to tonsil tissue resolved the spatially distinct organization of immune cell types and states in lymphoid follicles and extrafollicular zones. This technology progresses spatial biology by enabling spatially resolved chromatin accessibility profiling to improve our understanding of cell identity, cell state and cell fate decision in relation to epigenetic underpinnings in development and disease.

  • Deng, Y., Bartosovic, M., Ma, S., Zhang, D., Kukanja, P., Xiao, Y., Su, G., Liu, Y., Qin, X., Rosoklija, B.R, Dwork, A., Mann, J.J., Xu, M.L., Halene, S., Craft, J.E., Leong, W.K., Boldrini, M., Castelo-Branco, G. and Fan, R. Spatial profiling of chromatin accessibility in mouse and human tissues, Nature, 609: 375–383 (2022).
  1. Spatially resolved transcriptomics and proteomics

In multicellular systems, cells do not function in isolation but are strongly influenced by spatial location and surroundings. Spatial gene expression heterogeneity plays an essential role in a range of biological, physiological, and pathological processes. We developed a novel microfluidic platform (DBiT-seq) to deliver molecular barcodes to formaldehyde or FFPE fixed tissue sections in a spatially confined manner, enabling simultaneous barcoding of mRNAs and proteins, and construction of a high-spatial-resolution multi-omics atlas by NGS sequencing. The unique microfluidic in-tissue barcoding technique has enabled high-spatial-resolution mapping of whole transcriptome and tens of proteins at cellular level.

  • Liu, Y#, Yang, M#, Deng, Y#, Su, G, Enninful, A, Guo, C, Tebaldi, T, Zhang, D, Kim, D, Bai, Z, Norris, E, Pan, A, Li, J, Xiao, Y, Halene, S and Fan, R. “High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue”, Cell, 183: 1–17 (2020).

Research Interest

The Deng lab is developing novel technologies for spatial omics to solve challenging biological problems, including cancer and neurodegenerative diseases.

Vikram Paralkar, MD

Research Interest

Research in the Paralkar Lab spans the spectrum from human patient sample studies and mouse models to cutting-edge molecular biology tools, high-throughput sequencing approaches, and novel computational algorithms, all with the goal of gaining insight into how the transcription of coding genes and noncoding ribosomal DNA genes is regulated in hematopoietic stem cells, myeloid progenitors, and in leukemia.

rRNA Transcription in Hematopoiesis and Leukemia

Ribosomal RNA (rRNA) forms the majority of cellular RNA, and its transcription in the nucleolus by RNA Polymerase I from ribosomal DNA (rDNA) repeats accounts for the bulk of all transcription. rRNA transcription rates vary dramatically between different normal cell types in the hematopoietic tree, and leukemic cells have characteristic prominent nucleoli, indicating robust ribosome synthesis.

The rate of ribosome production has far-reaching influence on the fate of the cell, and dictates its size, proliferation, and ability to translate global or specific mRNAs. Little is known however about how rRNA transcription is regulated and fine-tuned across normal and malignant tissues, and whether this regulation can be targeted for leukemia treatment.

The Paralkar Lab has identified that key hematopoietic and leukemic transcription factors bind to rDNA and regulate rRNA transcription, and we are interested in understanding how the binding of cell-type-specific transcription factors regulates the activity of Polymerase I and the transcription of rRNA in normal hematopoiesis, and how this regulation is co-opted in leukemia to drive abundant ribosome biogenesis.

Stemness and Differentiation in Hematopoiesis and Leukemia

Normal hematopoiesis requires an intricate balance in the bone marrow between the ability of stem cells to maintain themselves for decades of life while producing billions of mature blood cells every day. This balance is maintained by the combinatorial activity of transcription factors and chromatin proteins that dictate the transcription of coding gene networks instructing fate choice decisions. Several of the critical factors involved in these decisions are mutated in acute and chronic leukemias, and their mutations tip the equilibrium in the bone marrow towards accumulation of aberrant progenitor populations.

The Paralkar Lab is interested in gaining a detailed mechanistic understanding of how chromatin proteins regulate the stemness-differentiation balance, and how mutations in them produce malignancy.

Bioinformatic Pipelines for Genetics and Epigenetics

Current bioinformatic pipelines for high-throughput studies like whole genome sequencing, RNA-seq, ChIP-seq, and single cell RNA-seq are limited in their ability to map repetitive elements of the genome like ribosomal DNA. Such loci therefore tend to be ignored in genome wide analyses. Given that rRNA accounts for the bulk of the transcriptional output of the cell, the inability to map datasets to rDNA has historically been a major limitation, and has created a significant knowledge gap in our understanding of the most abundant RNA in the cell.

The Paralkar Lab has developed customized genomes and computational pipelines to map datasets to rDNA, and we are interested in developing advanced tools to map and interpret the genetic and epigenetic profiles of rDNA in normal and malignant cells.

Selected Publications

George SS, Pimkin M, Paralkar VR: Customized genomes for human and mouse ribosomal DNA mapping. BioRxiv Nov 2022.

Antony C, George SS, Blum J, Somers P, Thorsheim CL, Wu-Corts DJ, Ai Y, Gao L, Lv K, Tremblay MG, Moss T, Tan K, Wilusz JE, Ganley ARD, Pimkin M, Paralkar VR: Control of ribosomal RNA synthesis by hematopoietic transcription factors. Molecular Cell 82(20): 3826-3839, Oct 2022.

Lv K, Gong C, Antony C, Han X, Ren J, Donaghy R, Cheng Y, Pellegrino S, Warren AJ, Paralkar VR, Tong W: HectD1 controls hematopoietic stem cell regeneration by coordinating ribosome assembly and protein synthesis. Cell Stem Cell 28: 1-16, Jul 2021.

Xu P, Palmer LE, Lechauve C, Zhao G, Yao Y, Luan J, Vourekas A, Tan H, Peng J, Scheutz JD, Mourelatos Z, Wu G, Weiss MJ, Paralkar VR: Regulation of gene expression by miR-144/451 during mouse erythropoiesis. Blood 133(23): 2518-2528, Jun 2019.

Traxler EA, Thom CS, Yao Y, Paralkar V, Weiss MJ: Non-specific inhibition of erythropoiesis by short hairpin RNAs. Blood 131(24): 2733-2736, Jun 2018.

Paralkar VR, Taborda CC, Huang P, Yao Y, Kossenkov AV, Prasad R, Luan J, Davies JO, Hughes JR, Hardison RC, Blobel GA, Weiss MJ: Unlinking an lncRNA from Its Associated cis Element. Molecular Cell 62(1): 104-10, Apr 2016.

Paralkar VR, Mishra T, Luan J, Yao Y, Kossenkov AV, Anderson SM, Dunagin M, Pimkin M, Gore M, Sun D, Konuthula N, Raj A, An X, Mohandas N, Bodine DM, Hardison RC, Weiss MJ: Lineage and species-specific long noncoding RNAs during erythro-megakaryocytic development. Blood 123(12): 1927-37, Mar 2014.

Kahlilia Morris-Blanco, Ph.D.

Research Interest

Dr. Morris-Blanco’s laboratory investigates epigenetic mechanisms involved in stroke pathophysiology by examining the interplay between spatial and temporal epigenetic dynamics, transcriptional regulation, and mitochondrial function in the post-stroke brain. Using both in vitro and in vivo experimental stroke models, they employ gene-specific and genome wide assessments of epigenomic organization, single-cell omics, metabolomics, and functional assessments of mitochondria and neuroprotection. Dr. Morris-Blanco is especially interested in using these mechanistic studies to develop novel treatment strategies, with the goal of translating epigenetic therapies to the clinic.

Shuo Zhang, Ph.D.

Research interest/work responsibility
My main role at the Penn Epigenetics Institute is to provide bioinformatic services to the Institute’s core members. I am interested in analyzing next-generation sequencing data to gain biological insights. In addition, I am interested in training and applying machine learning models to tackle biological questions.


  • Whole genome bisulfite sequencing, DNA methylation ChIP array
  • Bulk/single-cell RNA-seq, small RNA-seq (piRNA)
  • ChIP-seq, CUT&RUN, ATAC-seq
  • HiC


I have ten years of hands-on experience in analyzing a variety of next-generation sequencing data. During my PhD research in genomics, I developed a novel computational pipeline to annotate transposable elements (DNA parasites accounting for ~45% of the human genome) from a terabyte-scale whole genome re-sequencing D. melanogaster strains. During my first postdoctoral training, I developed computational pipelines that used HiC data to identify three-dimensional chromatin changes, including split/merge of topologically associated domains (TADs) and changes in chromatin stripes. During my postdoctoral training under the mentorship of Dr. Elizabeth A Heller, I expanded my expertise towards computational analysis of neuronal epigenetic regulation. This training involved analysis and mining of CUT&RUN, ChIP-seq and RNA-seq data. I have also expanded my expertise into machine learning models, such as PLIER, to interrogate cocaine-regulated gene expression in preclinical models of addiction.

1 2 3 8
Previous Next
Test Caption
Test Description goes like this