Postdoctoral Fellow, Associate Research Scientist – Kaile Wang Lab

Associate Research Fellow/Postdoctoral fellow:
1.Possess (or are about to obtain) a Ph.D. in a related field such as molecular and cellular biology, computational biology, genomics, bioengineering, machine learning, AI or an interdisciplinary discipline.
2.Proficient in high-throughput sequencing, single-cell sequencing, and/or multi-omics experimental and computational methods. Candidates with experience in high-throughput sequencing technologies, single-cell sequencing, spatial omics sequencing, algorithm development, artificial intelligence, large language modal, and cancer genomics are preferred.
2. Passionate about scientific research, curious about the unknown, enjoy exploration, and have the ambition to become an PI.
3.With publications (or upcoming publications) as the first author in SCI-indexed journals.
4. Talented, highly motivated, responsible, and a team player, capable of conducting independent research under the guidance of the PI.

We are continually accepting applications in our group. Please send your CV and cover letter to [email protected].

Understanding the origin and evolution of tumor cells is crucial for comprehending cancer initiation, recurrence, and for guiding subsequent tumor prevention and treatment strategies. Tumor cells arise from the accumulation of mutations within normal cells and then undergo multiple stages of evolution under various selective pressures, including the microenvironment, surgery, radiotherapy, chemotherapy, and immunotherapy. Studying this evolutionary process can unveil the mechanisms of cancer occurrence and progression, identify genome-related events tied to the origin and evolution of cancer cells, and ultimately provide new therapeutic targets.

In previous work, our research group developed Arc-well, the first high-throughput single cell DNA sequencing technology that is compatible with FFPE tissues; SNuBar, a method for labeling the spatial location or sample information of single cells, as well as multiple high-throughput sequencing-based low-frequency mutation detection methods (o2n-seq, Droplet-CirSeq, Oseq, and EasyMF) to systematically study tumor evolution. These studies identified persistent subclones during the progression of primary ductal carcinoma in situ (DCIS) to breast cancer recurrence, revealed a dominant evolutionary bottleneck model, and identified genomic variations associated with breast cancer recurrence, providing new therapeutic targets.

Our group focuses on developing and applying high-throughput single cell multi-omics and spatial sequencing technologies to address fundamental questions on tumor cell origin and evolution, including the epithelial cell-of-origin of tumor cells, the role of somatic mutations in cancer initiation, the spatial localization of tumor subclones, their interactions with the microenvironment, and the relationship between aging and cancer. Beyond cancer, we are fascinated by using the unique single-cell multi-omics datasets we are producing to digitize single cells, further exploring the underlying principles of cell behaviors.