Systematically quantifying morphological features reveals constraints on organoid phenotypes.

Organoids recapitulate complex 3D organ structures and represent a unique opportunity to probe the principles of self-organization. While we can alter an organoid's morphology by manipulating the culture conditions, the morphology of an organoid often resembles that of its original organ, suggesting that organoid morphologies are governed by a set of tissue-specific constraints. Here, we establish a framework to identify constraints on an organoid's morphological features by quantifying them from microscopy images of organoids exposed to a range of perturbations. We apply this framework to Madin-Darby canine kidney cysts and show that they obey a number of constraints taking the form of scaling relationships or caps on certain parameters. For example, we found that the number, but not size, of cells increases with increasing cyst size. We also find that these constraints vary with cyst age and can be altered by varying the culture conditions. We observed similar sets of constraints in intestinal organoids. This quantitative framework for identifying constraints on organoid morphologies may inform future efforts to engineer organoids.

Responsiveness to perturbations is a hallmark of transcription factors that maintain cell identity in vitro.

Identifying the particular transcription factors that maintain cell type in vitro is important for manipulating cell type. Identifying such transcription factors by their cell-type-specific expression or their involvement in developmental regulation has had limited success. We hypothesized that because cell type is often resilient to perturbations, the transcriptional response to perturbations would reveal identity-maintaining transcription factors. We developed perturbation panel profiling (P3) as a framework for perturbing cells across many conditions and measuring gene expression responsiveness transcriptome-wide. In human iPSC-derived cardiac myocytes, P3 showed that transcription factors important for cardiac myocyte differentiation and maintenance were among the most frequently upregulated (most responsive). We reasoned that one 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. We propose that responsiveness to perturbations is a property of transcription factors that help maintain cellular identity in vitro. A record of this paper's transparent peer review process is included in the supplemental information.

Genetic screening for single-cell variability modulators driving therapy resistance.

Cellular plasticity describes the ability of cells 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 new 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.

Quanti.us: a tool for rapid, flexible, crowd-based annotation of images.

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.

Multiplexed modular genetic targeting of quantum dots.

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.