A prominent trend in single-cell transcriptomics is providing spatial context alongside a characterization of each cell's molecular state. This typically requires targeting an a priori selection of genes, often covering less than 1% of the genome, and a key question is how to optimally determine the small gene panel. We address this challenge by introducing a flexible deep learning framework, PERSIST, to identify informative gene targets for spatial transcriptomics studies by leveraging reference scRNA-seq data. Using datasets spanning different brain regions, species, and scRNA-seq technologies, we show that PERSIST reliably identifies panels that provide more accurate prediction of the genome-wide expression profile, thereby capturing more information with fewer genes. PERSIST can be adapted to specific biological goals, and we demonstrate that PERSIST's binarization of gene expression levels enables models trained on scRNA-seq data to generalize with to spatial transcriptomics data, despite the complex shift between these technologies.

Motivation: A significant focus of biological research is to understand the development, organization and function of tissues. A particularly productive area of study is on single layer epithelial tissues in which the adherence junctions of cells form a 2D manifold that is fluorescently labeled. Given the size of the tissue, a microscope must collect a mosaic of overlapping 3D stacks encompassing the stained surface. Downstream interpretation is greatly simplified by preprocessing such a dataset as follows: (a) extracting and mapping the stained manifold in each stack into a single 2D projection plane, (b) correcting uneven illumination artifacts, (c) stitching the mosaic planes into a single, large 2D image, and (d) adjusting the contrast.

Results: We have developed PreMosa, an efficient, fully automatic pipeline to perform the four preprocessing tasks above resulting in a single 2D image of the stained manifold across which contrast is optimized and illumination is even. Notable features are as follows. First, the 2D projection step employs a specially developed algorithm that actually finds the manifold in the stack based on maximizing contrast, intensity and smoothness. Second, the projection step comes first, implying all subsequent tasks are more rapidly solved in 2D. And last, the mosaic melding employs an algorithm that globally adjusts contrasts amongst the 2D tiles so as to produce a seamless, high-contrast image. We conclude with an evaluation using ground-truth datasets and present results on datasets from Drosophila melanogaster wings and Schmidtae mediterranea ciliary components.

Availability: PreMosa is available under https://cblasse.github.io/premosa.

Contact: [email protected], [email protected].

Induced pluripotent stem cell (iPSC)-based models are powerful tools to study neurodegenerative diseases such as Parkinson's disease. The differentiation of patient-derived neurons and astrocytes allows investigation of the molecular mechanisms responsible for disease onset and development. In particular, these two cell types can be mono- or co-cultured to study the influence of cell-autonomous and non-cell-autonomous contributors to neurodegenerative diseases. We developed a streamlined procedure to produce high-quality/high-purity cultures of dopaminergic neurons and astrocytes that originate from the same population of midbrain floor-plate progenitors. This unit describes differentiation, quality control, culture parameters, and troubleshooting tips to ensure the highest quality and reproducibility of research results. © 2019 The Authors. Basic Protocol 1: Differentiation of iPSCs into midbrain-patterned neural progenitor cells Support Protocol: Quality control of neural progenitor cells Basic Protocol 2: Differentiation of neural progenitor cells into astrocytes Basic Protocol 3: Differentiation of neural progenitor cells into dopaminergic neurons Basic Protocol 4: Co-culture of iPSC-derived neurons and astrocytes.

Animals of the same species exhibit similar behaviours that are advantageously adapted to their body and environment. These behaviours are shaped at the species level by selection pressures over evolutionary timescales. Yet, it remains unclear how these common behavioural adaptations emerge from the idiosyncratic neural circuitry of each individual. The overall organization of neural circuits is preserved across individuals because of their common evolutionarily specified developmental programme. Such organization at the circuit level may constrain neural activity, leading to low-dimensional latent dynamics across the neural population. Accordingly, here we suggested that the shared circuit-level constraints within a species would lead to suitably preserved latent dynamics across individuals. We analysed recordings of neural populations from monkey and mouse motor cortex to demonstrate that neural dynamics in individuals from the same species are surprisingly preserved when they perform similar behaviour. Neural population dynamics were also preserved when animals consciously planned future movements without overt behaviour and enabled the decoding of planned and ongoing movement across different individuals. Furthermore, we found that preserved neural dynamics extend beyond cortical regions to the dorsal striatum, an evolutionarily older structure. Finally, we used neural network models to demonstrate that behavioural similarity is necessary but not sufficient for this preservation. We posit that these emergent dynamics result from evolutionary constraints on brain development and thus reflect fundamental properties of the neural basis of behaviour.

In 2015, a novel way to convert photoconvertible fluorescent proteins was reported that uses the intercept of blue and far-red light instead of traditional violet or near-UV light illumination. This Minireview describes and contrasts this distinct two-step mechanism termed primed conversion with traditional photoconversion. We provide a comprehensive overview of what is known to date about primed conversion and focus on the molecular requirements for it to take place. We provide examples of its application to axially confined photoconversion in complex tissues as well as super-resolution microscopy. Further, we describe why and when it is useful, including its advantages and disadvantages, and give an insight into potential future development in the field.

Information processing in the neocortex is performed by GABAergic interneurons that are integrated with excitatory neurons into precisely structured circuits. To reveal how each neuron type shapes sensory representations, we measured spikes and membrane potential of specific types of neurons in the barrel cortex while mice performed an active, whisker-dependent object localization task. Whiskers were tracked with millisecond precision. Fast-spiking (FS) neurons were activated by touch with short latency and by whisking. FS neurons track thalamic input and provide feedforward inhibition. Somatostatin (SOM)-expressing neurons were also excited by touch, but with a delay (5 ms) compared to excitatory (E) and FS neurons. SOM neurons monitor local excitation and provide feedback inhibition. Vasoactive intestinal polypeptide (VIP)-expressing neurons were not driven by touch but elevated their spike rate during whisking, disinhibiting E and FS neurons. Our data reveal rules of recruitment for specific interneuron types, providing foundations for understanding cortical computations.

A fundamental task in sequence analysis is to calculate the probability of a multiple alignment given a phylogenetic tree relating the sequences and an evolutionary model describing how sequences change over time. However, the most widely used phylogenetic models only account for residue substitution events. We describe a probabilistic model of a multiple sequence alignment that accounts for insertion and deletion events in addition to substitutions, given a phylogenetic tree, using a rate matrix augmented by the gap character. Starting from a continuous Markov process, we construct a non-reversible generative (birth-death) evolutionary model for insertions and deletions. The model assumes that insertion and deletion events occur one residue at a time. We apply this model to phylogenetic tree inference by extending the program dnaml in phylip. Using standard benchmarking methods on simulated data and a new "concordance test" benchmark on real ribosomal RNA alignments, we show that the extended program dnamlepsilon improves accuracy relative to the usual approach of ignoring gaps, while retaining the computational efficiency of the Felsenstein peeling algorithm.

Acetylcholine (ACh) acts through receptors to modulate a variety of neuronal processes, but it has been challenging to link ACh receptor function with subcellular location within cells where this function is carried out. To study the subcellular location of nicotinic ACh receptors (nAChRs) in native brain tissue, an optical method was developed for precise release of nicotine at discrete locations near neuronal membranes during electrophysiological recordings. Patch-clamped neurons in brain slices are filled with dye to visualize their morphology during 2-photon laser scanning microscopy, and nicotine uncaging is executed with a light flash by focusing a 405 nm laser beam near one or more cellular membranes. Cellular current deflections are measured, and a high-resolution three-dimensional (3D) image of the recorded neuron is made to allow reconciliation of nAChR responses with cellular morphology. This method allows for detailed analysis of nAChR functional distribution in complex tissue preparations, promising to enhance the understanding of cholinergic neurotransmission.