Artificial intelligence (AI) and single-cell studies have been making waves in the science and technology communities. AI offers a broad range of methods that can be used to investigate diverse data- and hypothesis-driven questions in single-cell biology. The highly heterogeneous nature of single-cell data can be analyzed across a wide range of research topics by generalizing deep-learning model design and optimization in a hypothesis-free manner. Our lab focuses on the research of single-cell multi-omics data, aiming to develop cutting-edge computational tools to discover underlying mechanisms in diverse biological systems.
Ma, Q., Xu, D. Deep learning shapes single-cell data analysis. Nat Rev Mol Cell Biol (2022)
|1. Regulatory mechanisms in complex tissues. Our lab focuses on developing computational methods to discover heterogeneous transcriptional regulatory mechanisms from single-cell sequencing data. Our efforts are split between inferring cell-type-specific regulatory signals and constructing reliable gene regulatory networks via the integration of single-cell multi-omics. We are also going into the development of novel deep learning algorithms for enhancer-GRN construction and rare cell population discoveries in cases, such as aging cells and minimal residue diseases. https://bmblx.bmi.osumc.edu/iris
Grants: R01GM131399-01 (PI), U54AG075931 (Core PI), NSF1945971 (PI)
|2. Immuno-oncology. Single-cell multi-omics has brought transformative insights into immuno-oncology, demonstrating success in describing novel immune subsets and defining important regulators of antitumor immunity. One significant challenge in immuno-oncology is identifying the heterogeneity of immune cells in tumors and their differentiation process. To overcome these limitations, a scMulti-omics study can offer detailed identification of diverse immune subsets at a higher resolution and provide an opportunity to understand the contribution of immune cells to tumor progression. Our lab endeavors single-cell applications in immuno-oncological areas. https://bmblx.bmi.osumc.edu/sc-io
Grants: R01CA262069-01 (Co-I), R01AI162779-02 (Co-I), U24CA252977 (MPI)
|3. Graph representation learning of spatial omics data. Spatially resolved technologies (e.g., Visium or CosMx MSI) provide new ways to study cell-cell and cell-environment relations in human diseases at either the cellular or tissue level. It is challenging to understand how a tissue (or an organ) forms and organizes functional regions and the underlying cellular and molecular mechanism from spatial omics data. Our project aims to explore graph representation methods along with deep neural networks to investigate tissue structures and functional behaviors. The long-term goal is to harmonize cellular and tissue biology and demystify how Intra-/extra-cellular activity affects tissue development or disease progress from spatial and single-cell multi-omics. https://bmblx.bmi.osumc.edu/spatial
Grants: R21HG012482 (MPI)
|4. Neurodegenerative disease. Single-cell RNA-sequencing (scRNA-seq) and single-nucleus RNA-sequencing (snRNA-seq) studies have provided remarkable insights into understanding human brains. Our lab takes the advantage of single-cell multi-omics and spatial transcriptomic data to discover the mystery of the molecular mechanisms in neural systems and the pathogenesis of Alzheimer’s disease (AD). https://bmblx.bmi.osumc.edu/neuron
Grants: R01MH124870-02 (Co-I), R01AG075092 (Co-I), R01DK126008-02 (Co-I)
|5. Microbiome and host interactions. We are developing enabling tools and databases, using metagenomic and metatranscriptomic data, to elucidate microbial systems and their interactions with human diseases. Our goal is to deliver reproducible and integrated analyses of complex gut microbiome data, detect functional and taxonomic abundances (biomarkers) in a microbial community, and infer significant associations between clinical measurements of human diseases and transformed microbial biomarkers. https://bmblx.bmi.osumc.edu/magical
Grants: R01AI171027-01A1 (Co-I), R01MH129589-01 (Co-I), CCTS Pilot (PI; 2020 completed)
[Jun-10-2023] Congratulations to Megan McNutt and her team from The Ohio State University, who were among the top three finalists in the American Society for Artificial Internal Organs: For Young Innovators (ASAIOfyi) Student Design Competition. They presented their groundbreaking project, “A Removal Device for Tunneled Intravascular Catheters,” at the American Society for Artificial Internal Organs Conference in San Francisco.
[Jun-6-2023] The International Conference on Intelligent Biology and Medicine (ICIBM 2023) accepts four of BMBL’s work for presentations:
Dr. Qin Ma will present a poster on “Single-cell biological network inference using a heterogeneous graph transformer“, which was recently recognized among the top 50 cancer papers by Nature Communications.
Dr. Anjun Ma will present a poster on “Deep Transfer Learning of Cancer Drug Responses by Integrating Bulk and Single-cell RNA-seq data“, published in Nature Communications.
Yuzhou Chang will present a poster on “Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning“, published in the Computational and Structural Biotechnology Journal.
Cankun Wang will give an oral presentation on “A Weighted Two-stage Sequence Alignment Framework to Identify DNA Motifs from ChIP-exo Data“, accepted as a regular paper in ICIBM 2023.
[Jun-1-2023] Yuzhou Chang is set to present an intriguing poster titled “Spatial omics feature representation using graph Fourier transform” at the Spatial Biology 2023 US Conference.
[May-29-2023] We are thrilled to announce Dr. Anjun Ma’s commencement in his new role as Clinical Assistant Professor at The Ohio State University, marking an exciting milestone in his professional journey!
[May-25-2023] The paper “scREAD: A Single-Cell RNA-Seq Database for Alzheimer’s Disease” earns a spot on SSRN’s Top Ten download list in the NeurosciRN: Neurodegenerative Disorders & Injuries (Topic).
[May-22-2023] Congratulations Yi Jiang’s triumphant graduation from Shandong University, where he majored in Software Engineering!
[May-21-2023] Congratulations to Xiaoying Wang for successfully completing her PhD dissertation! We commend her exceptional scholarly achievement and profound dedication to her academic journey.
[May-19-2023] Congratulations to Dr. Qin Ma on his promotion to Full Professor by The Ohio State University! We celebrate his outstanding accomplishments and unwavering dedication to his field.
[May-17-2023] Exciting development at BMBL as Megan McNutt accepts the position of Senior Research Technician, commencing her role from June 5, 2023.
[May-13-2023] Dr. Yang Li’s paper entitled “A Weighted Two-stage Sequence Alignment Framework to Identify DNA Motifs from ChIP-exo Data” has been accepted as a regular paper for oral presentation at the International Conference on Intelligent Biology and Medicine (ICIBM 2023).
[May-4-2023] Dr. Jordan Krull had been selected as a Pelotonia Post-Doctoral Scholar through the Pelotonia Scholars Program. Congratulations to Dr. Krull on this achievement!
[Apr-19-2023] The poster “The tumor microbiome associates with features of the tumor microenvironment, treatment outcomes, and histologies; a national collaboration of the exORIEN Consortium” was presented at the 2023 American Association for Cancer Research Annual Meeting.
[Apr-13-2023] Dr. Qin Ma will give an invited keynote presentation “Graph Representation Learning of Single-Cell Omics Data” at the 21st Asia Pacific Bioinformatics Conference (APBC 2023) .
[Apr-10-2023] Hao Cheng presented his project Identifying cell-type-specific senescent cells and signature genes using heterogeneous graph contrastive learning and received the second place poster award in the poster session at the TriState SenNet Annual Conference.
[Mar-22-2023] Our DeepMAPS paper is highlighted by Nature Communications editor, as one of the 50 best papers in the field of Cancer research published at Nature Communications.
[Mar-20-2023] Dr. Qin Ma will give an invited presentation “Graph Representation Learning of Single-cell Omics Data” at the School of Artificial Intelligence at Jilin University.
[Mar-16-2023] The published article “IRIS-EDA: An integrated RNA-Seq interpretation system for gene expression data analysis” has been selected to appear in the PLOS Showcase on Kudos.
[Mar-16-2023] Dr. Qin Ma will give an invited presentation “Single-cell biological network inference using deep learning” at the 2023 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference in Dallas, TX.