Diverse clonal fates emerge upon drug treatment of homogeneous cancer cells.

Even among genetically identical cancer cells, resistance to therapy frequently emerges from a small subset of those cells1-7. Molecular differences in rare individual cells in the initial population enable certain cells to become resistant to therapy7-9; however, comparatively little is known about the variability in the resistance outcomes. Here we develop and apply FateMap, a framework that combines DNA barcoding with single-cell RNA sequencing, to reveal the fates of hundreds of thousands of clones exposed to anti-cancer therapies. We show that resistant clones emerging from single-cell-derived cancer cells adopt molecularly, morphologically and functionally distinct resistant types. These resistant types are largely predetermined by molecular differences between cells before drug addition and not by extrinsic factors. Changes in the dose and type of drug can switch the resistant type of an initial cell, resulting in the generation and elimination of certain resistant types. Samples from patients show evidence for the existence of these resistant types in a clinical context. We observed diversity in resistant types across several single-cell-derived cancer cell lines and cell types treated with a variety of drugs. The diversity of resistant types as a result of the variability in intrinsic cell states may be a generic feature of responses to external cues.

ClampFISH 2.0 enables rapid, scalable amplified RNA detection in situ.

RNA labeling in situ has enormous potential to visualize transcripts and quantify their levels in single cells, but it remains challenging to produce high levels of signal while also enabling multiplexed detection of multiple RNA species simultaneously. Here, we describe clampFISH 2.0, a method that uses an inverted padlock design to efficiently detect many RNA species and exponentially amplify their signals at once, while also reducing the time and cost compared with the prior clampFISH method. We leverage the increased throughput afforded by multiplexed signal amplification and sequential detection to detect 10 different RNA species in more than 1 million cells. We also show that clampFISH 2.0 works in tissue sections. We expect that the advantages offered by clampFISH 2.0 will enable many applications in spatial transcriptomics.

Preformation and epigenesis converge to specify primordial germ cell fate in the early Drosophila embryo.

A critical step in animal development is the specification of primordial germ cells (PGCs), the precursors of the germline. Two seemingly mutually exclusive mechanisms are implemented across the animal kingdom: epigenesis and preformation. In epigenesis, PGC specification is non-autonomous and depends on extrinsic signaling pathways. The BMP pathway provides the key PGC specification signals in mammals. Preformation is autonomous and mediated by determinants localized within PGCs. In Drosophila, a classic example of preformation, constituents of the germ plasm localized at the embryonic posterior are thought to be both necessary and sufficient for proper determination of PGCs. Contrary to this longstanding model, here we show that these localized determinants are insufficient by themselves to direct PGC specification in blastoderm stage embryos. Instead, we find that the BMP signaling pathway is required at multiple steps during the specification process and functions in conjunction with components of the germ plasm to orchestrate PGC fate.

Uncoupling neurogenic gene networks in the Drosophila embryo.

The EGF signaling pathway specifies neuronal identities in the Drosophila embryo by regulating developmental patterning genes such as intermediate neuroblasts defective (ind). EGFR is activated in the ventral midline and neurogenic ectoderm by the Spitz ligand, which is processed by the Rhomboid protease. CRISPR/Cas9 was used to delete defined rhomboid enhancers mediating expression at each site of Spitz processing. Surprisingly, the neurogenic ectoderm, not the ventral midline, was found to be the dominant source of EGF patterning activity. We suggest that Drosophila is undergoing an evolutionary transition in central nervous system (CNS)-organizing activity from the ventral midline to the neurogenic ectoderm.

Dynamical hallmarks of cancer: Phenotypic switching in melanoma and epithelial-mesenchymal plasticity.

Phenotypic plasticity was recently incorporated as a hallmark of cancer. This plasticity can manifest along many interconnected axes, such as stemness and differentiation, drug-sensitive and drug-resistant states, and between epithelial and mesenchymal cell-states. Despite growing acceptance for phenotypic plasticity as a hallmark of cancer, the dynamics of this process remains poorly understood. In particular, the knowledge necessary for a predictive understanding of how individual cancer cells and populations of cells dynamically switch their phenotypes in response to the intensity and/or duration of their current and past environmental stimuli remains far from complete. Here, we present recent investigations of phenotypic plasticity from a systems-level perspective using two exemplars: epithelial-mesenchymal plasticity in carcinomas and phenotypic switching in melanoma. We highlight how an integrated computational-experimental approach has helped unravel insights into specific dynamical hallmarks of phenotypic plasticity in different cancers to address the following questions: a) how many distinct cell-states or phenotypes exist?; b) how reversible are transitions among these cell-states, and what factors control the extent of reversibility?; and c) how might cell-cell communication be able to alter rates of cell-state switching and enable diverse patterns of phenotypic heterogeneity? Understanding these dynamic features of phenotypic plasticity may be a key component in shifting the paradigm of cancer treatment from reactionary to a more predictive, proactive approach.

Changes in chromatin accessibility are not concordant with transcriptional changes for single-factor perturbations.

A major goal in the field of transcriptional regulation is the mapping of changes in the binding of transcription factors to the resultant changes in gene expression. Recently, methods for measuring chromatin accessibility have enabled us to measure changes in accessibility across the genome, which are thought to correspond to transcription factor-binding events. In concert with RNA-sequencing, these data in principle enable such mappings; however, few studies have looked at their concordance over short-duration treatments with specific perturbations. Here, we used tandem, bulk ATAC-seq, and RNA-seq measurements from MCF-7 breast carcinoma cells to systematically evaluate the concordance between changes in accessibility and changes in expression in response to retinoic acid and TGF-ß. We found two classes of genes whose expression showed a significant change: those that showed some changes in the accessibility of nearby chromatin, and those that showed virtually no change despite strong changes in expression. The peaks associated with genes in the former group had lower baseline accessibility prior to exposure to signal. Focusing the analysis specifically on peaks with motifs for transcription factors associated with retinoic acid and TGF-ß signaling did not reduce the lack of correspondence. Analysis of paired chromatin accessibility and gene expression data from distinct paths along the hematopoietic differentiation trajectory showed a much stronger correspondence, suggesting that the multifactorial biological processes associated with differentiation may lead to changes in chromatin accessibility that reflect rather than driving altered transcriptional status. Together, these results show many gene expression changes can happen independently of changes in the accessibility of local chromatin in the context of a single-factor perturbation.

Structural Basis of Neurohormone Perception by the Receptor Tyrosine Kinase Torso.

In insects, brain-derived Prothoracicotropic hormone (PTTH) activates the receptor tyrosine kinase (RTK) Torso to initiate metamorphosis through the release of ecdysone. We have determined the crystal structure of silkworm PTTH in complex with the ligand-binding region of Torso. Here we show that ligand-induced Torso dimerization results from the sequential and negatively cooperative formation of asymmetric heterotetramers. Mathematical modeling of receptor activation based upon our biophysical studies shows that ligand pulses are "buffered" at low receptor levels, leading to a sustained signal. By contrast, high levels of Torso develop the signal intensity and duration of a noncooperative system. We propose that this may allow Torso to coordinate widely different functions from a single ligand by tuning receptor levels. Phylogenic analysis indicates that Torso is found outside arthropods, including human parasitic roundworms. Together, our findings provide mechanistic insight into how this receptor system, with roles in embryonic and adult development, is regulated.

In vivo severity ranking of Ras pathway mutations associated with developmental disorders.

Germ-line mutations in components of the Ras/MAPK pathway result in developmental disorders called RASopathies, affecting about 1/1,000 human births. Rapid advances in genome sequencing make it possible to identify multiple disease-related mutations, but there is currently no systematic framework for translating this information into patient-specific predictions of disease progression. As a first step toward addressing this issue, we developed a quantitative, inexpensive, and rapid framework that relies on the early zebrafish embryo to assess mutational effects on a common scale. Using this assay, we assessed 16 mutations reported in MEK1, a MAPK kinase, and provide a robust ranking of these mutations. We find that mutations found in cancer are more severe than those found in both RASopathies and cancer, which, in turn, are generally more severe than those found only in RASopathies. Moreover, this rank is conserved in other zebrafish embryonic assays and Drosophila-specific embryonic and adult assays, suggesting that our ranking reflects the intrinsic property of the mutant molecule. Furthermore, this rank is predictive of the drug dose needed to correct the defects. This assay can be readily used to test the strengths of existing and newly found mutations in MEK1 and other pathway components, providing the first step in the development of rational guidelines for patient-specific diagnostics and treatment of RASopathies.

A quantitative model of developmental RTK signaling.

Receptor tyrosine kinases (RTKs) control a wide range of developmental processes, from the first stages of embryogenesis to postnatal growth and neurocognitive development in the adult. A significant share of our knowledge about RTKs comes from genetic screens in model organisms, which provided numerous examples demonstrating how specific cell fates and morphologies are abolished when RTK activation is either abrogated or significantly reduced. Aberrant activation of such pathways has also been recognized in many forms of cancer. More recently, studies of human developmental syndromes established that excessive activation of RTKs and their downstream signaling effectors, most notably the Ras signaling pathway, can also lead to structural and functional defects. Given that both insufficient and excessive pathway activation can lead to abnormalities, mechanistic analysis of developmental RTK signaling must address quantitative questions about its regulation and function. Patterning events controlled by the RTK Torso in the early Drosophila embryo are well-suited for this purpose. This mini review summarizes current state of knowledge about Torso-dependent Ras activation and discusses its potential to serve as a quantitative model for studying the general principles of Ras signaling in development and disease.

How activating mutations affect MEK1 regulation and function.

The MEK1 kinase directly phosphorylates ERK2, after the activation loop of MEK1 is itself phosphorylated by Raf. Studies over the past decade have revealed a large number of disease-related mutations in the MEK1 gene that lead to tumorigenesis and abnormal development. Several of these mutations result in MEK1 constitutive activity, but how they affect MEK1 regulation and function remains largely unknown. Here, we address these questions focusing on two pathogenic variants of the Phe-53 residue, which maps to the well-characterized negative regulatory region of MEK1. We found that these variants are phosphorylated by Raf faster than the wild-type enzyme, and this phosphorylation further increases their enzymatic activity. However, the maximal activities of fully phosphorylated wild-type and mutant enzymes are indistinguishable. On the basis of available structural information, we propose that the activating substitutions destabilize the inactive conformation of MEK1, resulting in its constitutive activity and making it more prone to Raf-mediated phosphorylation. Experiments in zebrafish revealed that the effects of activating variants on embryonic development reflect the joint control of the negative regulatory region and activating phosphorylation. Our results underscore the complexity of the effects of activating mutations on signaling systems, even at the level of a single protein.

Piscis: a novel loss estimator of the F1 score enables accurate spot detection in fluorescence microscopy images via deep learning.

Single-molecule RNA fluorescence in situ hybridization (RNA FISH)-based spatial transcriptomics methods have enabled the accurate quantification of gene expression at single-cell resolution by visualizing transcripts as diffraction-limited spots. While these methods generally scale to large samples, image analysis remains challenging, often requiring manual parameter tuning. We present Piscis, a fully automatic deep learning algorithm for spot detection trained using a novel loss function, the SmoothF1 loss, that approximates the F1 score to directly penalize false positives and false negatives but remains differentiable and hence usable for training by deep learning approaches. Piscis was trained and tested on a diverse dataset composed of 358 manually annotated experimental RNA FISH images representing multiple cell types and 240 additional synthetic images. Piscis outperforms other state-of-the-art spot detection methods, enabling accurate, high-throughput analysis of RNA FISH-derived imaging data without the need for manual parameter tuning.

Synthetic DNA barcodes identify singlets in scRNA-seq datasets and evaluate doublet algorithms.

Single-cell RNA sequencing (scRNA-seq) datasets contain true single cells, or singlets, in addition to cells that coalesce during the protocol, or doublets. Identifying singlets with high fidelity in scRNA-seq is necessary to avoid false negative and false positive discoveries. Although several methodologies have been proposed, they are typically tested on highly heterogeneous datasets and lack a priori knowledge of true singlets. Here, we leveraged datasets with synthetically introduced DNA barcodes for a hitherto unexplored application: to extract ground-truth singlets. We demonstrated the feasibility of our framework, "singletCode," to evaluate existing doublet detection methods across a range of contexts. We also leveraged our ground-truth singlets to train a proof-of-concept machine learning classifier, which outperformed other doublet detection algorithms. Our integrative framework can identify ground-truth singlets and enable robust doublet detection in non-barcoded datasets.

Retrospective identification of cell-intrinsic factors that mark pluripotency potential in rare somatic cells.

Pluripotency can be induced in somatic cells by the expression of OCT4, KLF4, SOX2, and MYC. Usually only a rare subset of cells reprogram, and the molecular characteristics of this subset remain unknown. We apply retrospective clone tracing to identify and characterize the rare human fibroblasts primed for reprogramming. These fibroblasts showed markers of increased cell cycle speed and decreased fibroblast activation. Knockdown of a fibroblast activation factor identified by our analysis increased the reprogramming efficiency. We provide evidence for a unified model in which cells can move into and out of the primed state over time, explaining how reprogramming appears deterministic at short timescales and stochastic at long timescales. Furthermore, inhibiting the activity of LSD1 enlarged the pool of cells that were primed for reprogramming. Thus, even homogeneous cell populations can exhibit heritable molecular variability that can dictate whether individual rare cells will reprogram or not.

TGF-ß blockade drives a transitional effector phenotype in T cells reversing SIV latency and decreasing SIV reservoirs in vivo.

HIV-1 persistence during ART is due to the establishment of long-lived viral reservoirs in resting immune cells. Using an NHP model of barcoded SIVmac239 intravenous infection and therapeutic dosing of anti-TGFBR1 inhibitor galunisertib (LY2157299), we confirm the latency reversal properties of in vivo TGF-ß blockade, decrease viral reservoirs and stimulate immune responses. Treatment of eight female, SIV-infected macaques on ART with four 2-weeks cycles of galunisertib leads to viral reactivation as indicated by plasma viral load and immunoPET/CT with a 64Cu-DOTA-F(ab')2-p7D3-probe. Post-galunisertib, lymph nodes, gut and PBMC exhibit lower cell-associated (CA-)SIV DNA and lower intact pro-virus (PBMC). Galunisertib does not lead to systemic increase in inflammatory cytokines. High-dimensional cytometry, bulk, and single-cell (sc)RNAseq reveal a galunisertib-driven shift toward an effector phenotype in T and NK cells characterized by a progressive downregulation in TCF1. In summary, we demonstrate that galunisertib, a clinical stage TGF-ß inhibitor, reverses SIV latency and decreases SIV reservoirs by driving T cells toward an effector phenotype, enhancing immune responses in vivo in absence of toxicity.

Deconstructing synthetic biology across scales: a conceptual approach for training synthetic biologists.

Synthetic biology allows us to reuse, repurpose, and reconfigure biological systems to address society's most pressing challenges. Developing biotechnologies in this way requires integrating concepts across disciplines, posing challenges to educating students with diverse expertise. We created a framework for synthetic biology training that deconstructs biotechnologies across scales-molecular, circuit/network, cell/cell-free systems, biological communities, and societal-giving students a holistic toolkit to integrate cross-disciplinary concepts towards responsible innovation of successful biotechnologies. We present this framework, lessons learned, and inclusive teaching materials to allow its adaption to train the next generation of synthetic biologists.

ERK hyperactivation serves as a unified mechanism of escape in intrinsic and acquired CDK4/6 inhibitor resistance in acral lentiginous melanoma.

Patients with metastatic acral lentiginous melanoma (ALM) suffer worse outcomes relative to patients with other forms of cutaneous melanoma (CM), and do not benefit as well to approved melanoma therapies. Identification of cyclin-dependent kinase 4 and 6 (CDK4/6) pathway gene alterations in >60% of ALMs has led to clinical trials of the CDK4/6 inhibitor (CDK4i/6i) palbociclib for ALM; however, median progression free survival with CDK4i/6i treatment was only 2.2 months, suggesting existence of resistance mechanisms. Therapy resistance in ALM remains poorly understood; here we report hyperactivation of MAPK signaling and elevated cyclin D1 expression serve as a mechanism of intrinsic early/adaptive CDK4i/6i resistance. ALM cells that have acquired CDK4i/6i resistance following chronic treatment exposure also exhibit hyperactivation of the MAPK pathway. MEK and/or ERK inhibition increases CDK4i/6i efficacy against therapy naïve and CDK4i/6i-resistant AM cells in xenograft and patient-derived xenograft (PDX) models and promotes a defective DNA repair, cell cycle arrested and apoptotic program. Notably, gene alterations poorly correlate with protein expression of cell cycle proteins in ALM or efficacy of CDK4i/6i, urging additional strategies when stratifying patients for CDK4i/6i trial inclusion. Concurrent targeting of the MAPK pathway and CDK4/6 represents a new approach for patients with metastatic ALM to improve outcomes.

Metabolic engineering for enhanced hydrogen production: a review.

Hydrogen gas exhibits potential as a sustainable fuel for the future. Therefore, many attempts have been made with the aim of producing high yields of hydrogen gas through renewable biological routes. Engineering of strains to enhance the production of hydrogen gas has been an active area of research for the past 2 decades. This includes overexpression of hydrogen-producing genes (native and heterologous), knockout of competitive pathways, creation of a new productive pathway, and creation of dual systems. Interestingly, genetic mutations in 2 different strains of the same species may not yield similar results. Similarly, 2 different studies on hydrogen productivities may differ largely for the same mutation and on the same species. Consequently, here we analyzed the effect of various genetic modifications on several species, considering a wide range of published data on hydrogen biosynthesis. This article includes a comprehensive metabolic engineering analysis of hydrogen-producing organisms, namely Escherichia coli, Clostridium, and Enterobacter species, and in addition, a short discussion on thermophilic and halophilic organisms. Also, apart from single-culture utilization, dual systems of various organisms and associated developments have been discussed, which are considered potential future targets for economical hydrogen production. Additionally, an indirect contribution towards hydrogen production has been reviewed for associated species.

Toward a systems-level probing of tumor clonality.

Cancer has been described as a genetic disease that clonally evolves in the face of selective pressures imposed by cell-intrinsic and extrinsic factors. Although classical models based on genetic data predominantly propose Darwinian mechanisms of cancer evolution, recent single-cell profiling of cancers has described unprecedented heterogeneity in tumors providing support for alternative models of branched and neutral evolution through both genetic and non-genetic mechanisms. Emerging evidence points to a complex interplay between genetic, non-genetic, and extrinsic environmental factors in shaping the evolution of tumors. In this perspective, we briefly discuss the role of cell-intrinsic and extrinsic factors that shape clonal behaviors during tumor progression, metastasis, and drug resistance. Taking examples of pre-malignant states associated with hematological malignancies and esophageal cancer, we discuss recent paradigms of tumor evolution and prospective approaches to further enhance our understanding of this spatiotemporally regulated process.

Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools.

Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.