BMSIP Projects 2023
2023
Data Harmonization with Machine Learning in Alzheimer’s Disease top
PI: Jinying Chen
Intern: Suraj Prabhu
This internship is in collaboration with the Chobanian & Avedisian School of Medicine Data Science Core The development of Alzheimer’s disease (AD) can span many years and the disease currently lacks effective treatments. Identifying people with risk in cognitive decline and progression to AD at the early stage may enable earlier intervention and better prognosis via modifiable risk factors and therefore reduce the disease burden. This internship will advance the study that develops natural language processing (NLP) and machine learning models to harmonize data from different resources.
Machine Learning Algorithms for Alzheimer’s Progression Risk Prediction top
PI: Jinying Chen
Intern: Vijetha Balakundi
This internship is in collaboration with the Chobanian & Avedisian School of Medicine Data Science Core The development of Alzheimer’s disease (AD) can span many years before symptom manifestation and clinical diagnosis. Effective treatments for AD are considered controversial and only minimally slow down progression. Assessing and identifying people with risk in cognitive decline and progression along the AD disease continuum may enable earlier intervention and better prognosis via modifiable risk factors and therefore reduce the disease burden. This internship will work on developing interpretable machine learning for risk prediction in AD progression.
In silico Cell Segmentation of Spatial Transcriptomics in Triple Negative Breast Cancer Tumors top
PI: Ruben Dries
Intern: ChihWei Fan
This project focuses on solid cancer tissues that display a complex structural architecture with both cellular and non-cellular components. By creating super-resolved spatial datasets with a resolution at the single transcript level (~ 150 nm) we can distinguish both cancer and non-cancer cells, including immune, adipocytes and stromal cells, organized within small niches and larger tissue structures. In addition to the transcript coordinate information, obtained from 500 unique genes, the datasets also contain immuno-fluorescence image stains that mark the cellular nucleus and cellular components. Most of our spatial datasets are from primary tumor samples obtained from patients at Boston Medical Center and diagnosed with triple negative breast cancer, which is the most aggressive form of breast cancer and known to be very heterogeneous.
Genetics of Super-resilience in Creutzfeldt-Jakob Disease top
PI: David Harris/Gustav Mostoslavsky
Intern: Avarind Sundaravadivelu
In this project we analyze DNA and iPSCs derived from 23 members of an Israeli kindred with an inherited form of Creutzfeldt-Jakob Disease caused by the E200K mutation. Two mutation carriers from this kindred lived into their 90s, many standard deviations beyond the typical age of disease onset and death (~55 years old). We hypothesize that these “super-resilient” individuals harbor one or a small number of gene variants that provide a powerful disease-protective effect. We aim to identify these variants using whole-exome sequence data we have obtained from all members of this family. We will also be obtaining WES data from several families in which a parent and child are both E200K carriers, but the parent is super-resilient, while the child is not. These will be particularly helpful in identifying protective gene variants.
Mechanisms of Growth and Differentiation of Melanocytes in Melanoma top
PI: Deborah Lang
Intern: Khushi Ahuja
We are examining how upstream factors regulate genes through genetic enhancers and at steps after RNA transcription to control the levels of proteins involved with cell growth and differentiation in melanoma and melanocytes. We examine pathways in normal melanocytes and in transformed melanoctyes that give rise to melanoma cells. This internship will address 1) what DNA regulatory sites are regulated by YAP and TAZ, to control downstream genes with transcriptional cofactors. 2) How is PAX3 regulating gene expression through acting as an upstream transcription factor and post-transcriptionally by interacting with RNA binding proteins to regulate RNA splicing and regulation.
Therapeutic Capacity of Extracellular Vesicles after Cortical Injury top
PI: Tara Moore
Intern: Sonal Dinesh Khanna
Changes in the aged brain that occur with stroke, head injury or Alzheimer’s Disease Related Dementias result in chronic cognitive and motor deficits. We have shown that mesenchymal stem cells-derived extracellular vesicles (EVs) enhance recovery of motor function following cortical injury in aged female monkeys. EVs reduced injury-induced including microglial neuroinflammation, neuronal excitotoxicity, synapse loss, oligodendrocyte damage, and myelination deficits. However, the molecular mechanisms, through which EVs promote therapeutic effects on different populations of brain cells following cortical injury are unknown. This internship will address how the treatment with EVs following injury affects the recovery of the brain at the level of a single-cell resolution. Brain samples of rhesus monkeys treated and untreated with EVs will be analyzed. The results will contribute to the development of new therapeutic approaches based on the utilization of EVs.
Molecular Mechanisms sirtuin-1 in Aortic Aneurysm top
PI: Francesca Seta
Intern: Pooja Paresh Savla
This project is focused on understanding the cellular and molecular mechanisms of aortic aneurysms, vascular conditions that can lead to death when they suddenly dissect/rupture. We identified sirtuin-1 as a key protein involved in the protection of the aortic wall against aortic dissection/rupture, via its potent anti-inflammatory and anti-oxidant effects. Therefore, sirtuin-1 is a promising therapeutic target to prevent dissection/rupture in individuals with aortic aneurysms. However, we still need to fully understand the signaling pathways activated by sirtuin-1 mediating its beneficial effects.
Molecular Mechanisms of Immune Suppression by AhR in Oral and Lung Cancers top
PI: David Sherr
Intern: Vinay Kumar Duggineni
The projects are related to the molecular mechanisms through which an environmental chemical receptor, the AhR, suppresses immune responses to oral and lung cancers. We have shown in cell lines and in mouse models that the AhR influences expression of PD-L1 and other immune checkpoints that suppress tumor immunity. The AhR does this by regulating expression of several genes known to be involved in cancer signaling. In general, we need computational expertise to analyze multiple genomic data sets that we have accumulated in our lab and then to extrapolate the results to the human condition by mining, for example, human gene (TCGA) and protein (CPTAC) data sets.
Interactive meta-analysis web tool for gene expression-based contextual classification of cellular phenotypes top
PI: Tuan Leng Tay
Intern: Krupa Sampat
Although there has been tremendous progress in gene sequencing technologies to study cellular properties, organ formation and disease pathogenesis, biomedical researchers mostly focus on limited datasets restricted to their specialisations. We propose a meta-analysis framework to leverage information across vast gene expression datasets by noise reduction and transcript alignment. This strategy could improve experimental design, reveal consensus gene regulatory mechanisms of cellular processes, consolidate our knowledge on transcriptional changes, and unveil new targets for detailed mechanistic studies. As proof-of-principle, we started this project using ~40 published transcriptomic data sets from the brain-resident macrophages known as microglia, which we have been studying for over a decade. Microglial cells undergo rapid context-dependent molecular and morphological changes in their subpopulations during the lifespan of the organism and in disease. This project will identify core gene regulatory modules by comparing gene datasets of proliferative and plastic cell types, to uncover novel genes, motifs or physiological contexts that promote these cellular characteristics. As these cells exert positive (e.g., organ growth, recovery) or destructive outcomes in various circumstances, the results may contribute towards precision therapy for wound healing, chronic inflammation, and cancer.
Understanding the Epigenetic Landscape of Down Syndrome using Cortical Organoids top
PI: Ella Zeldich
Intern: Anna McNiff
Abnormal oligodendrocyte differentiation and deficient myelin production were recently identified as potential contributors to development of intellectual disability, a devastating hallmark of Down Syndrome (DS). The presence of an extra chromosome and the triplication of specific genes exerts global changes in the transcriptomic and epigenetic landscape that interfere with the fundamental establishment and development of central nervous system cells. We will be using cortical organoids in vitro system generated from patient-derived induced pluripotent stem cell to examine how changes in the epigenetic machinery alter oligodendrocyte biology in Down Syndrome. The internship will address how the DS-associated epigenetic landscape shapes the developmental and functional trajectories of oligodendrocytes and other cell types populating cortical organoids. We plan to uncover how the presence of additional chromosome 21 affects transcriptional dynamics and accessibility of chromatin in DS.
Markers of Aging in Mouse and Monkey Models of Alzheimer’s Disease top
PI: Chao Zhang
Intern: Jou-Hsuan Lee
This internship is in collaboration with the Chobanian & Avedisian School of Medicine Data Science Core This project involves analyzing existing and ongoing datasets from mouse and monkey brains to identify transcriptional markers of aging and Alzheimer’s disease, and to better understand the relationship between healthy aging and Alzheimer’s disease. We have generated several datasets from different mouse and monkey brain regions in various projects related to the aforementioned tasks. We need to analyze these datasets using standard scRNASeq analysis packages such as Seurat in R or Scanpy in Python. The intern will be responsible for not only discovering the biological markers related to healthy aging or Alzheimer’s disease, but also comparing the results from the two tasks.
Computational pipeline for a novel single-cell RNASeq protocol top
PI: Chao Zhang
Intern: Zedong Lin
This internship is in collaboration with the Chobanian & Avedisian School of Medicine Data Science Core My lab and my collaborator’s lab have generated single-cell RNA sequencing data using the recently published sci-RNA-seq3 protocol. However, unlike well-established commercial protocols like the 10X platform, there is no comprehensive package available to process the data. This internship will establish a computational pipeline to process the raw data, perform comprehensive quality control, and convert the data to a standard format.