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Tang Lab

Our research is inspired by the observation that early life experiences have profound impact on the behavioral patterns, repertoire of subjective experiences and mental health outcomes later in life. To understand how experience shapes the brain, behavior, mental health and ultimately consciousness, we integrate approaches from artificial intelligence, structural biology, bioengineering, developmental and behavioral neuroscience and psychology/psychiatry.

Research Projects

Engineering Conditionally Stable Sensors and Effectors

We combine high-throughput, high-content screening and rational engineering strategies to create panels of sensors and effectors targeting biomolecules of interest.

The diverse cell types that make up complex organisms and the diverse cellular compartments within cells can often be distinguished by their specific molecular compositions. To facilitate access to various parts of cells and of organisms, we engineer molecular tools that are active in the presence of target molecules of interest. Our initial efforts have focused on nanobodies, antibody fragments derived from Camelid species. Nanobodies are ideal for biomedical applications due to their encoding of antigen-binding in a single polypeptide chain, and the relative ease with which they can be expressed inside cells. Using nanobodies as building blocks for synthetic biology, we demonstrated that one could build cell type-specific systems that are active only in specific cell populations expressing a target molecule. We applied these systems to repurpose transgenic GFP animal lines as tools for cell type-specific manipulation, bypassing the need to create traditional effector transgenic lines such as Cre- or Flp- expressing animals. We later demonstrated that nanobodies can be rendered conditionally stable by modifying their conserved and non-epitope binding framework regions, greatly simplifying delivery and enhancing generalizability. Going forward, we will enhance the generalizability of the conditional stability approach by systematically generating novel binders, optimizing engineering strategies and building bioinformatics resources for the community. We are further interested in expanding the conditionally stable strategy to other binder systems. Collectively, these efforts will result in a large panel of reagents, bioinformatics tools and generalizable engineering strategies suitable for any biomedical application that require target-specific sensing or manipulation across cells and organisms.

Neural Mechanisms underlying the Action Learning process

We apply state-of-the-art technologies to study the neural and behavioral mechanisms behind how animals learn to home in on specific actions that lead to reward.

We aim to apply our engineered molecular sensors and effectors in our efforts to understand how animals learn about the world. Animals have the remarkable ability to home in on reward-causing actions and action sequences as a result of exploration and experience. However, the process by which animals assign credits to actions have been elusive, due to confounding factors in traditional operant conditioning paradigms. We overcame these obstacles by developing a closed loop reinforcement system that strips down reinforcement rules to their most minimal requirements (ex. Action > Reward, instead of Action sequences + Place / Cue / Manipulandum interactions > Reward). The closed loop system combines wireless inertial sensors, unsupervised behavioral classification and optogenetics to convert behavior detection into cell-type specific stimulation of dopamine neurons. Using this closed loop system in mice, we discovered behavioral dynamics behind how animals assign credit to specific actions and action sequences that lead to reward. Our findings point to the importance of the refinement process in homing in on the right actions, and appreciation for latent learning in more difficult action learning problems. Going forward, we aim to integrate state-of-the-art neural recording technologies (ex. miniscopes, neuropixels, fiber photometry) to probe the neural computations behind action credit assignment. These efforts have the potential to reveal neural populations, circuits, and behavioral patterns to target for diagnosis and therapy in neurological and psychiatric conditions where action learning is impaired (ex. rigid behavior in autism and personality disorders, obsessive-compulsive disorder, addiction, etc).

AI-mediated modeling of psychological structures

We integrate physical sciences, artificial intelligence, and psychology/psychiatry to study the self.

Each of us have been subjected to a lifelong learning process that shapes the way our brain functions, and in turn, the way we perceive the world. One of the limitations of animal models has been the inability to probe the animal’s presumed subjective experiences. Self-report surveys have long probed the repertoire of subjective experiences in humans, but past efforts have been limited in scope and faced scalability challenges. We recently integrated artificial intelligence, psychology and other fields to turn phenomenology research into a hard science with physical relevance. The repertoire of subjective experiences is altered in individuals subjected to adverse early life experiences (ex. those with PTSD, Borderline Personality Disorder, Dissociative Disorders, etc) – we aim to apply our efforts in this direction to rigorously study the patterns of alterations and link it to physically relevant treatments.


Action Learning

Tang JCY, Paixao V, Carvalho F, Silva A, Klaus A, da Silva JA, Costa RM. Dynamic behaviour restructuring mediates dopamine-dependent credit assignment. Nature. 2024 Feb;626(7999):583-592. doi: 10.1038/s41586-023-06941-5. Epub 2023 Dec 13. PMID: 38092040; PMCID: PMC10866702.



Dingus JG, Tang JCY, Amamoto R, Wallick GK, Cepko CL. A general approach for stabilizing nanobodies for intracellular expression. Elife. 2022 Nov 23;11:e68253. doi: 10.7554/eLife.68253. PMID: 36416528; PMCID: PMC9683787.

Tang JC, Drokhlyansky E, Etemad B, Rudolph S, Guo B, Wang S, Ellis EG, Li JZ, Cepko CL. Detection and manipulation of live antigen-expressing cells using conditionally stable nanobodies. Elife. 2016 May 20;5:e15312. doi: 10.7554/eLife.15312. PMID: 27205882; PMCID: PMC4922844.

Tang JC, Rudolph S, Dhande OS, Abraira VE, Choi S, Lapan SW, Drew IR, Drokhlyansky E, Huberman AD, Regehr WG, Cepko CL. Cell type-specific manipulation with GFP-dependent Cre recombinase. Nat Neurosci. 2015 Sep;18(9):1334-41. doi: 10.1038/nn.4081. Epub 2015 Aug 10. PMID: 26258682; PMCID: PMC4839275.

Tang JC, Szikra T, Kozorovitskiy Y, Teixiera M, Sabatini BL, Roska B, Cepko CL. A nanobody-based system using fluorescent proteins as scaffolds for cell-specific gene manipulation. Cell. 2013 Aug 15;154(4):928-39. doi: 10.1016/j.cell.2013.07.021. PMID: 23953120; PMCID: PMC4096992.

Book Chapter

Tang JCY, Rudolph S, Cepko CL. Viral Delivery of GFP-Dependent Recombinases to the Mouse Brain. Methods Mol Biol. 2017;1642:109-126. doi: 10.1007/978-1-4939-7169-5_8. PMID: 28815497.

Developmental Neuroscience

Berndt AJ, Tang JC, Ridyard MS, Lian T, Keatings K, Allan DW. Gene Regulatory Mechanisms Underlying the Spatial and Temporal Regulation of Target-Dependent Gene Expression in Drosophila Neurons. PLoS Genet. 2015 Dec 29;11(12):e1005754. doi: 10.1371/journal.pgen.1005754. PMID: 26713626; PMCID: PMC4694770.

Castellanos MC, Tang JC, Allan DW. Female-biased dimorphism underlies a female-specific role for post-embryonic Ilp7 neurons in Drosophila fertility. Development. 2013 Sep;140(18):3915-26. doi: 10.1242/dev.094714. PMID: 23981656; PMCID: PMC3915572.

View a complete list of Dr. Tang’s publications on PubMed.

Join the Lab

We are looking for individuals who are passionate about learning and growing and solving longstanding problems in science.  Please send your CV and cover letter to Jonathan Tang to inquire about opportunities. 

Meet the Tang Lab Team

Contact Us

Jonathan Tang, PhD

For questions or inquiries,

Physical Address

Center for Integrative Brain Research
1900 Ninth Ave.
Seattle, WA 98101

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