目前聚焦的科研项目以胚胎培养液游离DNA（cfDNA）为研究对象，应用高通量测序和机器学习等方法，探究植入前胚胎cfDNA的生物学特征与胚胎质量的内在关联，明确胚胎发育过程中cfDNA片段化的动态调控机制，构建基于培养液cfDNA生物学特征多模态融合的优质胚胎筛选模型。本研究解决当前建立基于微量培养液cfDNA的高灵敏度无创胚胎筛选方法遇到的瓶颈问题，为推动IVF无创检测的临床转化提供理论和实验依据。主要研究内容：

1.植入前胚胎培养液cfDNA生物学特征及与体外胚胎胚胎质量的关系。

2.植入前胚胎发育时程中cfDNA甲基化对片段降解的调控机制。

3.基于植入前胚胎培养液cfDNA多模态特征的无创胚胎筛选模型构建。

1、国家自然科学基金青年项目，孕妇外周血胎儿DNA的生物学特征用于无创产前诊断的研究，2019/01-2021/12，结题，主持。

2、江苏省自然科学基金面上项目，胎儿DNA分离方法及其在无创产前诊断中的应用研究，2021/07-2024/06，在研，主持。

3、国家重点研发计划项目，重大呼吸慢病动物模型生物样本库和多模分析数据库建立，2023/01-2027/12，在研，参加。

4、国家自然科学基金面上项目，LncRNAHCP5编码蛋白调控14-3-3ζ促进三阴乳腺癌恶性进展的机制研究，2020/01-2023/12，结题，参加。

5、国家自然科学基金面上项目，基于三代高通量测序的环状RNA可翻译性研究，2019/01-2022/12，结题，参加。

1.      Pan M, Shi H, Liu Z, Dong J, Cai L, Ge Q: The integrity of cfDNA infollicular fluid and spent medium from embryo culture is associated with embryograde in patients undergoing in vitro fertilization. J Assist Reprod Genet 2022,38(12):3113-3124. 

2.      Pan M, Lu J, Liu Z, Shi H, Bai Y, Chen P, Ge Q: Integrity of cell-freeDNA in maternal plasma extracellular vesicles as a potential biomarker fornon-invasive prenatal testing. Int J Gynaecol Obstet 2021, 158(2):406-417.

3.      Pan M, Chen P, Lu J, Liu Z, Jia E, Ge Q: The fragmentation patterns ofmaternal plasma cell-free DNA and its applications in non-invasive prenataltesting. Prenat Diagn 2020, 40(8):911-917. 

4.      Pan M, Schwartzman JA, Dunn AK, Lu Z, Ruby EG: A Single Host-Derived GlycanImpacts Key Regulatory Nodes of Symbiont Metabolism in a Coevolved Mutualism. mBio 2015,6(4):e00811. 

5.      Shi H, Pan M, Jia E, LuW, Zhou Y, Sheng Y, Zhao X, Cai L, Ge Q: A comprehensive characterization ofcell-free RNA in spent blastocyst medium and quality prediction for blastocyst.ClinSci (Lond) 2023, 137(2):129-120. 

6.      Qi T, Pan M, Shi H, WangL, Bai Y, Ge Q: Cell-free DNA fragmentomics: the novel promising biomarker. Int JBiol Sci 2023, 24(2):1503

7.      Shi H, Ge Q, Pan M, ShengY, Qi T, Zhou Y, Sun Y, Bai Y, Cai L: Agarose amplificaiton based sequencingcharacterization cell-free RNA in preimplantation spent embryo medium. Anal Chim Acta 2024, 1926

8.      Shi H, Pan M, Sheng Y,Jia E, Wang Y, Dong J, Tu J, Bai Y, Cai L, Ge Q: Extracellular cell-free RNAprofile in human large follicles and small follicles. Front Cell Dev Biol 2022,10:940336. 

9.      Zhou Y, Qi T, Pan M, TuJ, Zhao X, Ge Q: Deep-Cloud: a deep neural network-based approach for analyzingdifferentially expressed genes of RNA-seq data. J Chem Inf Model 2023. 

10.   Zhou Y, Jia E, Sheng Y, Qiao Y, Wang Y, Shi H, Liu Z, Pan M, Tu J, Bai Y, Ge Q: Sensitiveand Low-Bias Transcriptome Sequencing Using Agarose PCR. ACS Appl Mater Interfaces 2022,14(17):19154-19167. 

11.   Jia E, Zhou Y, Shi H, Pan M,Zhao X, Ge Q: Effects of brain tissue section processing and storage time ongene expression. Anal Chim Acta 2021, 1142:38-47. 

12.   Zhou Y, Jia E, Qiao Y, Shi H, Liu Z, Pan M, Zhao X, Bai Y, Ge Q: Low bias multiple displacementamplification with confinement effect based on agarose gel. AnalBioanal Chem 2021, 413(17):4397-4405. 

13.   Zhou Y, Jia E, Pan M,Zhao X, Ge Q: Encoding Method of Single-cell Spatial Transcriptomics Sequencing.IntJ Biol Sci 2020, 16(14):2663-2674. 

14.   Shi H, Zhou Y, Jia E, Pan M,Bai Y, Ge Q: Bias in RNA-seq Library Preparation: Current Challenges andSolutions. Biomed Res Int 2021, 2021:6647597. 

15.   Jia E, Pan M, Liu Z, ZhouY, Zhao X, Dong J, Bai Y, Ge Q: Transcriptomic profiling of differentiallyexpressed genes and related pathways in different brain regions in Parkinson's disease.NeurosciLett 2020, 732:135074. 

16.   Liu Z, Wang Z, Jia E, Ouyang T, PanM, Lu J, Ge Q, Bai Y: Analysis of genome-wide in cell free DNA methylation:progress and prospect. Analyst 2019, 144(20):5912-5922.