The first meeting exclusively dedicated to the 'High-throughput dense reconstruction of cell lineages' took place at Janelia Research Campus (Howard Hughes Medical Institute) from 14 to 18 April 2019. Organized by Tzumin Lee, Connie Cepko, Jorge Garcia-Marques and Isabel Espinosa-Medina, this meeting echoed the recent eruption of new tools that allow the reconstruction of lineages based on the phylogenetic analysis of DNA mutations induced during development. Combined with single-cell RNA sequencing, these tools promise to solve the lineage of complex model organisms at single-cell resolution. Here, we compile the conference consensus on the technological and computational challenges emerging from the use of the new strategies, as well as potential solutions.

Optical imaging has become a powerful tool for studying brains . The opacity of adult brains makes microendoscopy, with an optical probe such as a gradient index (GRIN) lens embedded into brain tissue to provide optical relay, the method of choice for imaging neurons and neural activity in deeply buried brain structures. Incorporating a Bessel focus scanning module into two-photon fluorescence microendoscopy, we extended the excitation focus axially and improved its lateral resolution. Scanning the Bessel focus in 2D, we imaged volumes of neurons at high-throughput while resolving fine structures such as synaptic terminals. We applied this approach to the volumetric anatomical imaging of dendritic spines and axonal boutons in the mouse hippocampus, and functional imaging of GABAergic neurons in the mouse lateral hypothalamus .

BACKGROUND: Recent advancements with induced pluripotent stem cell-derived (iPSC) retinal pigment epithelium (RPE) have made disease modeling and cell therapy for macular degeneration feasible. However, current techniques for intracellular electrophysiology - used to validate epithelial function - are painstaking and require manual skill; limiting experimental throughput.

NEW METHOD: A five-stage algorithm, leveraging advances in automated patch clamping, systematically derived and optimized, improves yield and reduces skill when compared to conventional, manual techniques.

RESULTS: The automated algorithm improves yield per attempt from 17% (manually, n = 23) to 22% (automated, n = 120) (chi-squared, p = 0.004). Specifically for RPE, depressing the local cell membrane by 6 μm and electroporating (buzzing) just prior to this depth (5 μm) maximized yield.

COMPARISON WITH EXISTING METHOD: Conventionally, intracellular epithelial electrophysiology is performed by manually lowering a pipette with a micromanipulator, blindly, towards a monolayer of cells and spontaneously stopping when the magnitude of the instantaneous measured membrane potential decreased below a predetermined threshold. The new method automatically measures the pipette tip resistance during the descent, detects the cell surface, indents the cell membrane, and briefly buzzes to electroporate the membrane while descending, overall achieving a higher yield than conventional methods.

CONCLUSIONS: This paper presents an algorithm for high-yield, automated intracellular electrophysiology in epithelia; optimized for human RPE. Automation reduces required user skill and training while, simultaneously, improving yield. This algorithm could enable large-scale exploration of drug toxicity and physiological function verification for numerous kinds of epithelia.

Histone post-translational modifications are key gene expression regulators, but their rapid dynamics during development remain difficult to capture. We applied a Fab-based live endogenous modification labeling technique to monitor the changes in histone modification levels during zygotic genome activation (ZGA) in living zebrafish embryos. Among various histone modifications, H3 Lys27 acetylation (H3K27ac) exhibited most drastic changes, accumulating in two nuclear foci in the 64- to 1k-cell-stage embryos. The elongating form of RNA polymerase II, which is phosphorylated at Ser2 in heptad repeats within the C-terminal domain (RNAP2 Ser2ph), and miR-430 transcripts were also concentrated in foci closely associated with H3K27ac. When treated with α-amanitin to inhibit transcription or JQ-1 to inhibit binding of acetyl-reader proteins, H3K27ac foci still appeared but RNAP2 Ser2ph and miR-430 morpholino were not concentrated in foci, suggesting that H3K27ac precedes active transcription during ZGA. We anticipate that the method presented here could be applied to a variety of developmental processes in any model and non-model organisms.

Each faculty recruiting season, many postdocs ask, "What is a chalk talk?" The chalk talk is many things-a sales pitch, a teaching demonstration, a barrage of questions, and a description of a future research program. The chalk talk is arguably the most important component of a faculty search interview. Yet few postdocs or grad students receive training or practice in giving a chalk talk. In the following essay, I'll cover the basics of chalk talk design and preparation.

Unprecedented technological advances in single-cell RNA-sequencing (scRNA-seq) technology have now made it possible to profile genome-wide expression in single cells at low cost and high throughput. There is substantial ongoing effort to use scRNA-seq measurements to identify the "cell types" that form components of a complex tissue, akin to taxonomizing species in ecology. Cell type classification from scRNA-seq data involves the application of computational tools rooted in dimensionality reduction and clustering, and statistical analysis to identify molecular signatures that are unique to each type. As datasets continue to grow in size and complexity, computational challenges abound, requiring analytical methods to be scalable, flexible, and robust. Moreover, careful consideration needs to be paid to experimental biases and statistical challenges that are unique to these measurements to avoid artifacts. This chapter introduces these topics in the context of cell-type identification, and outlines an instructive step-by-step example bioinformatic pipeline for researchers entering this field.

Idiosyncratic tendency to choose one alternative over others in the absence of an identified reason, is a common observation in two-alternative forced-choice experiments. It is tempting to account for it as resulting from the (unknown) participant-specific history and thus treat it as a measurement noise. Indeed, idiosyncratic choice biases are typically considered as nuisance. Care is taken to account for them by adding an ad-hoc bias parameter or by counterbalancing the choices to average them out. Here we quantify idiosyncratic choice biases in a perceptual discrimination task and a motor task. We report substantial and significant biases in both cases. Then, we present theoretical evidence that even in idealized experiments, in which the settings are symmetric, idiosyncratic choice bias is expected to emerge from the dynamics of competing neuronal networks. We thus argue that idiosyncratic choice bias reflects the microscopic dynamics of choice and therefore is virtually inevitable in any comparison or decision task.

We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance.

Rotaviruses, like other non-enveloped, double-strand RNA (dsRNA) viruses, package an RNA-dependent RNA polymerase (RdRp) with each duplex of their segmented genomes. Rotavirus cell entry results in loss of an outer protein layer and delivery into the cytosol of an intact, inner capsid particle (the “double-layer particle” or DLP). The RdRp, designated VP1, is active inside the DLP; each VP1 achieves many rounds of mRNA transcription from its associated genome segment. Previous work has shown that one VP1 molecule lies close to each fivefold axis of the icosahedrally symmetric DLP, just beneath the inner surface of its protein shell, embedded in tightly packed RNA. We have determined a high-resolution structure for the rotavirus VP1 RdRp in situ, by local reconstruction of density around individual fivefold positions. We have analyzed intact virions (“triple-layer particles” or TLPs), non-transcribing DLPs and transcribing DLPs. Outer layer dissociation enables the DLP to synthesize RNA, in vitro as well as in vivo, but appears not to induce any detectable structural change in the RdRp. Addition of NTPs, Mg2+, and S-adenosyl methionine, which allows active transcription, results in conformational rearrangements, in both VP1 and the DLP capsid shell protein, that allow a transcript to exit the polymerase and the particle. The position of VP1 (among the five symmetrically related alternatives) at one vertex does not correlate with its position at other vertices. This stochastic distribution of site occupancies limits long-range order in the 11-segment, dsRNA genome.

Glucose is arguably the most important molecule in metabolism, and its mismanagement underlies diseases of vast societal import, most notably diabetes. Although glucose-related metabolism has been the subject of intense study for over a century, tools to track glucose in living organisms with high spatio-temporal resolution are lacking. We describe the engineering of a family of genetically encoded glucose sensors with high signal-to-noise ratio, fast kinetics and affinities varying over four orders of magnitude (1 µM to 10 mM). The sensors allow rigorous mechanistic characterization of glucose transporters expressed in cultured cells with high spatial and temporal resolution. Imaging of neuron/glia co-cultures revealed ∼3-fold higher glucose changes in astrocytes versus neurons. In larval Drosophila central nervous system explants, imaging of intracellular neuronal glucose suggested a novel rostro-caudal transport pathway in the ventral nerve cord neuropil, with paradoxically slower uptake into the peripheral cell bodies and brain lobes. In living zebrafish, expected glucose-related physiological sequelae of insulin and epinephrine treatments were directly visualized in real time. Additionally, spontaneous muscle twitches induced glucose uptake in muscle, and sensory- and pharmacological perturbations gave rise to large but enigmatic changes in the brain. These sensors will enable myriad experiments, most notably rapid, high-resolution imaging of glucose influx, efflux, and metabolism in behaving animals.