Cellular plasticity describes cells’ ability to transition from one set of phenotypes to another. In melanoma, transient fluctuations in the molecular state of tumor cells mark the formation of rare cells primed to survive BRAF inhibition and reprogram into a stably drug resistant fate. However, the biological processes governing cellular priming remain unknown. We used CRISPR/Cas9 genetic screens to identify genes that affect cell fate decisions by altering cellular plasticity. We found that many factors can independently affect cellular priming and fate decisions. We discovered a novel, plasticity-based mode of increasing resistance to BRAF inhibition that pushes cells towards a more differentiated state. Manipulating cellular plasticity through inhibition of DOT1L before the addition of the BRAF inhibitor resulted in more therapy resistance than concurrent administration. Our results indicate that modulating cellular plasticity can alter cell fate decisions and may prove useful for treating drug resistance in other cancers.

We describe Quanti.us , a crowd-based image-annotation platform that provides an accurate alternative to computational algorithms for difficult image-analysis problems. We used Quanti.us for a variety of medium-throughput image-analysis tasks and achieved 10-50× savings in analysis time compared with that required for the same task by a single expert annotator. We show equivalent deep learning performance for Quanti.us-derived and expert-derived annotations, which should allow scalable integration with tailored machine learning algorithms.

Cellular plasticity describes the ability of cells to transition from one set of phenotypes to another. In the context of cancer therapeutics, plasticity refers to transient fluctuations in the molecular state of tumor cells, driving the formation of rare cells primed to survive drug treatment and ultimately reprogram into a stably resistant fate. However, the biological processes governing this cellular plasticity remain unknown. We used CRISPR/Cas9 genetic screens to reveal genes that affect cell fate decisions by altering cellular plasticity across a range of functional categories. We found that cellular plasticity and cell fate decision making can be decoupled in that factors can affect cell fate decisions in both plasticity-dependent and independent manners. We discovered a novel mode of altering resistance based on cellular plasticity that, contrary to known mechanisms, pushes cells towards a more differentiated state. We further confirmed our prediction that manipulating cellular plasticity before the addition of the main therapy would result in changes in therapy resistance more than concurrent administration. Together, our results indicate that identifying pathways modulating cellular plasticity has the potential to alter cell fate decisions and may provide a new avenue for treating drug resistance.

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Mucin-type O-glycosylation is initiated by the UDP-GalNAc polypeptide:N-acetylgalactosaminyltransferase (GalNAc-T) family of enzymes. Their activity results in the GalNAc α1-O-Thr/Ser structure, termed the Tn antigen, which is further decorated with additional sugars. In neoplastic cells, the Tn antigen is often overexpressed. Because O-glycosylation is controlled by the activity of GalNAc-Ts, their regulation is of great interest. Previous reports suggest that growth factors, EGF or PDGF, induce Golgi complex-to-endoplasmic reticulum (ER) relocation of both GalNAc-Ts and Tn antigen in HeLa cells, offering a mechanism for Tn antigen overexpression termed “GALA”. However, we were unable to reproduce these findings. Upon treatment of HeLa cells with either EGF or PDGF we observed no change in the co-localization of endogenous GalNAc-T1, GalNAc-T2 or Tn antigen with the Golgi complex marker TGN46. There was also no enhancement of localization with the ER marker calnexin. We conclude that growth factors do not cause redistribution of GalNAc-Ts from the Golgi complex to the ER in HeLa cells.

While DNA-directed nanotechnology is now a well-established platform for bioinspired nanoscale assembly in vitro, the direct targeting of various nanomaterials in living biological systems remains a significant challenge. Hybrid biological systems with integrated and targeted nanomaterials may have interesting and exploitable properties, so methods for targeting various nanomaterials to precise biological locations are required. Fluorescence imaging has benefited from the use of nanoparticles with superior optical properties compared to fluorescent organic dyes or fluorescent proteins. While single-particle tracking (SPT) in living cells with genetically encoded proteins is limited to very short trajectories, the high photon output of genetically targeted and multiplexed quantum dots (QDs) would enable long-trajectory analysis of multiple proteins. However, challenges with genetic targeting of QDs limit their application in these experiments. In this report, we establish a modular method for targeting QD nanoparticles selectively to multiple genetically encoded tags by precomplexing QD–streptavidin conjugates with cognate biotinylated hapten molecules. This approach enables labeling and SPT of multiple genetically encoded proteins on living cells at high speed and can label expressed proteins in the cytosol upon microinjection into living cells. While we demonstrate labeling with three distinct QD conjugates, the approach can be extended to other specific hapten–affinity molecule interactions and alternative nanoparticles, enabling precise directed targeting of nanoparticles in living biological systems.

AbstractOur ability to identify the particular transcription factors that maintain cell type is limited. Identification of factors by their cell type-specific expression or their participation in developmental regulation has been only modestly successful. We hypothesized that because cell type is often resilient to perturbations, the transcriptional response to perturbations would identify identity-maintaining factors. We developed Perturbation Panel Profiling (P3) as a framework for perturbing cells in dozens of conditions and measuring gene expression responsiveness transcriptome-wide. Applying P3 to human iPSC-derived cardiac myocytes showed that transcription factors known to function in cardiac differentiation and maintenance were among the most frequently up-regulated (most responsive). We reasoned that one potential function of responsive genes may be to maintain cellular identity. We identified responsive transcription factors in fibroblasts using P3 and found that suppressing their expression led to enhanced reprogramming efficiency. We propose that responsiveness to perturbations is a property of factors that help maintain cellular identity.