(6kr) Quantitative Single-Cell Analysis of RNA Regulation at the Single Molecule Level

Key words: Interdisciplinary (Systems Biology, Biophysics, Bioengineering, Deep learning, etc.), quantitative, single molecule, single mammalian cells

Research Interests:

I am passionate about quantitatively investigate important but previously inaccessible molecular and cellular biology questions, in vitro and in vivo, at the single molecule level in individual mammalian cells. My interdisciplinary background allows me to integrate diverse approaches (from molecular biology to deep learning, from time-lapse microscope to mammalian cell engineering, from photonics to integrated circuits like Arduino, etc.) to develop more sensitive and powerful single molecule tools to probe fundamental biological questions.

Selected Graduate Work: Single molecule manipulation --- in David Bensimon lab (ENS de Paris)

Motivation: Base-pairing sequence-structure relationship is essential for gene regulation. In particular, the competition between a hairpin and an adjacent hybrid is involved in many significant biological processes, such as replication arrest, CRISPR recognition, DNA origami, etc. However, due to the technical difficulty to monitor this dynamic competition process, the governing rules remains unclear.

Developed toolbox: I developed a method to characterize the dynamics of adjacent hybrids by monitoring the extension of (hybrid-blocked or closed) hairpin, using magnetic tweezers.

Achievement 1: My work provided a foundation to understand base-pairing competition. More specifically, I systematically quantified the dynamics of adjacent hybrid displacement at various conditions and proposed a stochastic step-by–step model to describe underlying mechanism.

Achievement 2: My work invented a novel 4^th generation sequencing method, providing the impetus and foundation for a biotech startup, Depixus@. The first commercial platform will be launched next year.

Selected Postdoc Work: Single cell RNA splicing regulation --- in Michael Elowitz lab (CalTech)

Motivation: In eukaryotes, splicing is ubiquitous. During splicing, nascent pre-mRNAs are processed to remove introns and/or exons. A key function of this splicing process is the ability to increase gene-product diversity by generating multiple distinct isoforms from a single gene, through alternatively splicing particular exons and(or) introns. However, the majority of individual introns (and exons) are constitutively spliced. It has remained unclear what functional roles such constitutive splicing provides.

Developed toolbox 1: I developed a method to measure splicing efficiency at the transcriptional active site in individual cells, using multiple-channel intensity-based single-molecule fluorescence in-situ hybridization (smFISH), by adapting skills from stellar photometry in crowded star fields.

Developed toolbox 2: I developed a label-free automatic segmentation pipeline using deep learning.

Achievement 1: My quantitative single-cell splicing analysis characterized the dependence of splicing efficiency on transcription level in individual cells and revealed that splicing exhibited an unexpected ‘economy of scale’ behavior. These results indicate constitutive splicing as a non-linear filter that amplifies the expression of genes when they are more strongly transcribed, playing an active functional role in modulating gene expression.

Achievement 2: I am collaborating with two other labs and using the developed methods to quantify splicing efficiency within the proximity of long noncoding RNA Malat1 (with Mitchell Guttman’s lab at CalTech), and compare allele-specific transcription level within the proximity of XIST (X-inactive specific transcript) (with Katherine Plath’s lab at UCLA), both at the single cell level.

Future directions:

Motivation: Single-cell RNA study has revealed many previously hidden or unappreciated events in population-based research, like the significance of cell-cell heterogeneity during embryonic and immune systems, the fundamental transcriptional noise in most cell types, etc. However, many important RNA species (e.g. circular RNAs, RNA modifications) remains challenging to study at the single cell level. This is because those RNAs have either too short length of specific sequences or too small expression in individual cells, causing technical difficulty for quantitative measurements. In my future lab, I aim to develop a new single-molecule RNA detection toolbox, identify those currently inaccessible RNA species and investigate their regulation at the single cell level.

Why me: My quantitative interdisciplinary background puts me in an optimal position to execute the proposed research program. My PhD training (base-pairing molecular programming in vitro) allows me to design reliable DNA probe-set combination, which is the foundation to develop the new RNA detection toolbox. My Postdoc work (quantifying single-cell splicing efficiency by smFISH) equips me with extensive experiences of smFISH technique and knowledge of single cell RNA regulation. More generally, my interdisciplinary training experience (including statistical physics, bioengineering, molecular biology, synthetic biology, machine learning, etc.) allows me to connect modeling and experimental systems in my work and communicate/collaborate with experts from different background.

Teaching Interests:

My interdisciplinary background allows me to teach or co-teach a broad range of topics, including biology-related interdisciplinary courses (e.g. Bioengineering and Biophysics, Computational Biology, Systems Biology), core biology courses (e.g. Molecular Biology, Genetics, Cell Biology), and many other math or physics courses that are useful for studying biological questions (e.g. Statistics, Mathematical Methods in Physics and Engineering, Classical Mechanics, Programming for Beginners [C, Python, MATLAB, Deep Learning], Optics). I can also teach advanced courses that merge modeling and experiments, with topics drawn from modern research, including but not limited to single molecule manipulation, quantitative single cell analysis, and novel bioengineering tools.

Education and Training:

1. California Institute of Technology, Postdoc, in Biology and Bioengineering, Pasadena, CA
2. École Normale Supérieure de Paris, Ph.D. in Biophysics, Paris
3. Massachussetts Institute of Technology, Master internship in Bioengineering, Boston, MA
4. École Normale Supérieure de Paris, Licence3, in Physics, Paris
5. Nanjing University, B.S. in Physics (rank 1^st out of 221), Nanjing, China

Invention Disclosures (Depixus @ has licensed all patents) :

1. Bensimon D, Croquette V, Allemand JF, Manosas M, Ding F, Method of DNA sequencing by polymerization, EP2390350A1, CA2800637A1, WO2011147929A1, EP2576822A1, CN103097551A, US20130171636A1, EP2576822B1
2. Bensimon D, Allemand JF, Manosas M, Ding F, Croquette V, Method of DNA sequencing by hybridization, EP2390351A1, CA2800639A1, WO2011147931A1, EP2576818A1, CN103052718A, US20130137098A1, et al.
3. Bensimon D, Allemand JF, Ding F, Croquette V, Method of DNA detection and quantification by single-molecule hybridization and manipulation, CA2859913A1, WO2013093005A1, EP2794915A1, US20150031028A1, et al.
4. Bensmion D, Croquette V, Gouet G, Allemand JF, Ding F, Process for detection of DNA modifications and protein binding by single molecule manipulation, CA2898151A1, WO2014114687A1

Selected Publications:

1. Ding F, Elowitz M, Constitutive splicing and economies of scale in gene expression, Nature Structural & Molecular Biology, (2019), 26, 424–432.
2. Ding F, et al., Single cell dynamics and functions of negative autoregulatory splicing, elife (in process)
3. Ding F, et al., Displacement and dissoci-ation of oligonucleotides during DNA hairpin closure under strain, Nature Chemistry (in process)
4. Ding F^*, Levine JH^*, Yi D, Linton J, Elowitz M, Label-free cell segmentation and tracking in various cell lines and imaging conditions by deep learning (in preparation)
5. Ding F, et al., Single-molecule mecha-nical identification and sequencing, Nature Methods, (2012) 9(4), 367-372. (News and Views: S Linnarsson, Nature Methods)
6. Manosas M, Spiering MM, Ding F, Bensimon D, Allemand JF, Benkovic SJ, Croquette V, Mechanism of strand displacement synthesis by DNA replicative polymerases, Nucleic Acids Research, (2012) 40 (13), 6174-6186.
7. Manosas M, Spiering MM, Ding F, Croquette V, Benkovic SJ, Collaborative coupling between polymerase and helicase for leading-strand synthesis, Nucleic Acids Research, (2012) 40 (13), 6187-6198.
8. Meglio A, Praly E, Ding F, Allemand JF, Bensimon D, Croquette V, Single DNA/protein studies with magnetic traps, Opin. Struct. Biol. (2009) 19(5):615-22.
9. Ferrer J*, Ding F*, Tarsa P, Brau R, Lang M, Interlaced Optical Trapping and Fluorescence Excitation improves fluorophore longevity, Pharm. Biotechnol. (2009) (5): 502-7. (*: authors contributed equally to publication)
10. Ding F, et al., Discussion and modification of experiment of measuring the heat conduction of coefficient of gas based on the hot-wire method, Physics Experimentation (2004) 24(12):39-41