Xing Lab publish in Science Advances: Quantify cell phenotypic transition dynamics

In this work the Xing Lab tackled an outstanding open question on if and how one can extract dynamical information from snapshot data.
They first developed a quantitative framework that integrates standard imaging facilities and state-of-the-art computational analysis approaches to extract high-dimensional dynamical features of single live cell trajectories. The ability of being “quantitative” and “high-dimensional” is critical for addressing the question mentioned above. The framework allows one to use the same mathematical language to quantitatively describe cell phenotypic transition dynamics as one describes particle motions in physics and chemistry. This a conceptual novelty sets up a new framework of studying the biological processes from a physics perspective. They studied the epithelial-to-mesenchymal transition, and identified two parallel paths for the transition process that are concealed from snapshot data due to cell-cell heterogeneity. The work demonstrates the importance of live cell studies, and our developed framework provides such a general quantitative platform.

Wang W, Douglas D, Zhang J, Chen YJ, Cheng YY, Kumari S, Enuameh MS, Dai Y, Wallace CT, Watkins SC, Shu W, Xing J. (2020) Live cell imaging and analysis reveal cell phenotypic transition dynamics inherently missing in snapshot data. Science Advances.

Drs. Joseph Ayoob and Joshua Kangas publish in PLoS Computational Biology

Dr. Joseph Ayoob, Associate Professor, Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh; and Dr. Joshua Kangas, Assistant Teaching Professor, Computational Biology Department, School of Computer Science, Carnegie Mellon University have published “10 Simple Rules for Teaching Wet-Lab Experimentation to Computational Biology Students (aka, turning computer mice into lab rats)” in PLoS Computational Biology.

Joe and Josh first became involved with the Lab Methods for Computational Biology class, which is part of the Carnegie-Mellon University of Pittsburgh Computational Biology Ph.D. program, over ten years ago – with Josh as a student and then teaching assistant and Joe as a new instructor. For the past three years they have been teaching the course together and wanted to share their years of expertise with the broader computational biology community.

Abstract: “Graduate students in Computational Biology typically have strong computational backgrounds but are frequently limited in their understanding of the theory, approach, and practice of biological experimentation used to generate data. A thorough understanding of the techniques used to generate biological data is essential for computational biologists to effectively critique and incorporate data into their research efforts. Furthermore, students are more frequently generating their own data in their PhD research making this background knowledge crucial for their success.  To give students this knowledge, insight, and experience, the ‘Laboratory Methods for Computational Biologists’ (LMCB) course was established as a core course in the CPCB curriculum to provide a hands-on, research-oriented laboratory experience in four major areas: genomics, microscopy and bioimaging, high content screening, and X-ray crystallography.  The LMCB course provides foundational and experiential wet-lab training for the benefit of nascent computational scientists.  In this article, we provide some of the guiding principles and approaches that we have used to establish, evolve, and shape the LMCB course.”

Congratulations Drs. Ayoob and Kangas on your publication and thank you for your continued dedication to education!

Ayoob JC, Kangas JD. (2020) 10 simple rules for teaching wet-lab experimentation to computational biology students, i.e., turning computer mice into lab rats. PLoS Comput Biol 16(6): e1007911.


Carvunis Lab Publish in Nature Communications

De novo emergence of adaptive membrane proteins from thymine-rich genomic sequences

Recent evidence demonstrates that novel protein-coding genes can arise de novo from nongenic loci. This evolutionary innovation is thought to be facilitated by the pervasive translation of non-genic transcripts, which exposes a reservoir of variable polypeptides to natural selection. We find that adaptive emerging sequences tend to encode putative transmembrane domains, and that thymine-rich intergenic regions harbor a widespread potential to produce transmembrane domains. These findings, together with in-depth examination of the de novo emerging YBR196C-A locus, suggest a novel evolutionary model whereby adaptive transmembrane polypeptides emerge de novo from thymine-rich nongenic regions and subsequently accumulate changes molded by natural selection.



Vakirlis N, Acar O, Hsu B, Coelho NC, Van Oss SB, Wacholder A, Medetgul-Ernar K, Bowman II RW, Hines CP, Iannotta J, Parikh SB, McLysaght A, Camacho CJ, O’Donnell AF, Ideker T, Carvunis AR. De novo emergence of adaptive membrane proteins from thymine-rich genomic sequences. Nat Commun 11, 781 (2020).

The Lee lab develop a dynamic stimulation system to probe signal transduction networks in single cells

Using open-source parts and 3D-printed components, the Lee lab develops a robotic system for mammalian cell cultures that accurately reproduces user-defined concentration profiles for one or more stimuli, such as cytokines or drugs. The team applies the dynamic stimulation system to investigate NF-kB signaling in single cells exposed to time-varying concentrations of TNF, a molecular network that is often deregulated in autoimmunity and cancer. Cellular responses to dynamic stimuli reveals context-dependent sensitivities and new classes of single cell responses that are distinct from the canonical NF-kB response during persistent stimulation. Guided by computational modeling, the team show that new response classes can be modulated with chemicals that target rates for basal cellular processes, including transcription and translation.

Taken together, the work shows that dynamic stimuli can be used to more accurately recapitulate biological complexity, to reveal hidden capabilities of biological systems, and to provide new opportunities to rationally manipulate disease-associated signaling mechanisms.

Mokashi CS, Schipper DL, Qasaimeh MA, Lee REC. A System for Analog Control of Cell Culture Dynamics to Reveal Capabilities of Signaling Networks. (2019) iScience [Epub ahead of print]