Divergent Learning-Related Transcriptional States of Cortical Glutamatergic Neurons.

Experience-dependent gene expression reshapes neural circuits, permitting the learning of knowledge and skills. Most learning involves repetitive experiences during which neurons undergo multiple stages of functional and structural plasticity. Currently, the diversity of transcriptional responses underlying dynamic plasticity during repetition-based learning is poorly understood. To close this gap, we analyzed single-nucleus transcriptomes of L2/3 glutamatergic neurons of the primary motor cortex after 3 d motor skill training or home cage control in water-restricted male mice. "Train" and "control" neurons could be discriminated with high accuracy based on expression patterns of many genes, indicating that recent experience leaves a widespread transcriptional signature across L2/3 neurons. These discriminating genes exhibited divergent modes of coregulation, differentiating neurons into discrete clusters of transcriptional states. Several states showed gene expressions associated with activity-dependent plasticity. Some of these states were also prominent in the previously published reference, suggesting that they represent both spontaneous and task-related plasticity events. Markedly, however, two states were unique to our dataset. The first state, further enriched by motor training, showed gene expression suggestive of late-stage plasticity with repeated activation, which is suitable for expected emergent neuronal ensembles that stably retain motor learning. The second state, equally found in both train and control mice, showed elevated levels of metabolic pathways and norepinephrine sensitivity, suggesting a response to common experiences specific to our experimental conditions, such as water restriction or circadian rhythm. Together, we uncovered divergent transcriptional responses across L2/3 neurons, each potentially linked with distinct features of repetition-based motor learning such as plasticity, memory, and motivation.

FOXA1/2 depletion drives global reprogramming of differentiation state and metabolism in a human liver cell line and inhibits differentiation of human stem cell-derived hepatic progenitor cells.

FOXA factors are critical members of the developmental gene regulatory network (GRN) composed of master transcription factors (TF) which regulate murine cell fate and metabolism in the gut and liver. How FOXA factors dictate human liver cell fate, differentiation, and simultaneously regulate metabolic pathways is poorly understood. Here, we aimed to determine the role of FOXA2 (and FOXA1 which is believed to compensate for FOXA2) in controlling hepatic differentiation and cell metabolism in a human hepatic cell line (HepG2). siRNA mediated knockdown of FOXA1/2 in HepG2 cells significantly downregulated albumin (p < .05) and GRN TF gene expression (HNF4α, HEX, HNF1ß, TBX3) (p < .05) and significantly upregulated endoderm/gut/hepatic endoderm markers (goosecoid [GSC], FOXA3, and GATA4), gut TF (CDX2), pluripotent TF (NANOG), and neuroectodermal TF (PAX6) (p < .05), all consistent with partial/transient reprograming. shFOXA1/2 targeting resulted in similar findings and demonstrated evidence of reversibility of phenotype. RNA-seq followed by bioinformatic analysis of shFOXA1/2 knockdown HepG2 cells demonstrated 235 significant downregulated genes and 448 upregulated genes, including upregulation of markers for alternate germ layers lineages (cardiac, endothelial, muscle) and neurectoderm (eye, neural). We found widespread downregulation of glycolysis, citric acid cycle, mitochondrial genes, and alterations in lipid metabolism, pentose phosphate pathway, and ketogenesis. Functional metabolic analysis agreed with these findings, demonstrating significantly diminished glycolysis and mitochondrial respiration, with concomitant accumulation of lipid droplets. We hypothesized that FOXA1/2 inhibit the initiation of human liver differentiation in vitro. During human pluripotent stem cells (hPSC)-hepatic differentiation, siRNA knockdown demonstrated de-differentiation and unexpectedly, activation of pluripotency factors and neuroectoderm. shRNA knockdown demonstrated similar results and activation of SOX9 (hepatobiliary). These results demonstrate that FOXA1/2 controls hepatic and developmental GRN, and their knockdown leads to reprogramming of both differentiation and metabolism, with applications in studies of cancer, differentiation, and organogenesis.

Effects of 3D electrodes arrangement in a novel AC electroosmotic micropump: Numerical modeling and experimental validation.

To date, a comprehensive systematic optimization framework, capable of accurately predicting an efficient electrode geometry, is not available. Here, different geometries, including 3D step electrodes, have been designed in order to fabricate AC electroosmosis micropumps. It is essential to optimize both geometrical parameters of electrode, such as width and height of steps on each base electrode and their location in one pair, the size of each base electrode (symmetric or asymmetric), the gap of electrode pairs, and nongeometrical parameters such as fluid flow in a channel and electrical characteristics (e.g., frequency and voltage). The governing equations comprising of electric domain and fluid domain have been coupled using finite element method. The developed model was employed to investigate the effect of electrode geometric parameters on electroosmotic slip velocity and its subsequent effect on pressure and flow rate. Numerical simulation indicates that the optimal performance can be achieved using a design with varying step height and displacement, at a given voltage (2.5 V) and frequency (1 kHz). Finally, in order to validate the numerical simulation, the optimal microchip was fabricated using a combination of photolithography, electroplating, and a polydimethylsiloxane microchannel. Our results indicate that our micropump is capable of generating a pressure, velocity, and flow rate of 74.2 Pa, 1.76 mm/s, and 14.8 µl/min, respectively. This result reveals that our proposed geometry outperforms the state-of-the-art micropumps previously reported in the literature by improving the fluid velocity by 32%, with 80% less electrodes per unit length, and whereas the channel length is ∼80% shorter.

Deep learning for neural decoding in motor cortex.

Objective. Neural decoding is an important tool in neural engineering and neural data analysis. Of various machine learning algorithms adopted for neural decoding, the recently introduced deep learning is promising to excel. Therefore, we sought to apply deep learning to decode movement trajectories from the activity of motor cortical neurons.Approach. In this paper, we assessed the performance of deep learning methods in three different decoding schemes, concurrent, time-delay, and spatiotemporal. In the concurrent decoding scheme where the input to the network is the neural activity coincidental to the movement, deep learning networks including artificial neural network (ANN) and long-short term memory (LSTM) were applied to decode movement and compared with traditional machine learning algorithms. Both ANN and LSTM were further evaluated in the time-delay decoding scheme in which temporal delays are allowed between neural signals and movements. Lastly, in the spatiotemporal decoding scheme, we trained convolutional neural network (CNN) to extract movement information from images representing the spatial arrangement of neurons, their activity, and connectomes (i.e. the relative strengths of connectivity between neurons) and combined CNN and ANN to develop a hybrid spatiotemporal network. To reveal the input features of the CNN in the hybrid network that deep learning discovered for movement decoding, we performed a sensitivity analysis and identified specific regions in the spatial domain.Main results. Deep learning networks (ANN and LSTM) outperformed traditional machine learning algorithms in the concurrent decoding scheme. The results of ANN and LSTM in the time-delay decoding scheme showed that including neural data from time points preceding movement enabled decoders to perform more robustly when the temporal relationship between the neural activity and movement dynamically changes over time. In the spatiotemporal decoding scheme, the hybrid spatiotemporal network containing the concurrent ANN decoder outperformed single-network concurrent decoders.Significance. Taken together, our study demonstrates that deep learning could become a robust and effective method for the neural decoding of behavior.

A systematic overview of electrode configuration in electric-driven micropumps.

Accurate manipulation of fluids in microfluidic devices is an important factor affecting their functions. Since the emergence of microfluidic technology to transport fluids in microchannels, the electric field has been utilized as an effective dynamic pumping mechanism. This review attempts to provide a fundamental insight of the various electric-driven flows in microchannels and their working mechanisms as micropumps for microfluidic devices. Different electrokinetic mechanisms implemented in electrohydrodynamic-, electroosmosis-, electrothermal, and dielectrophoresis-based micropumps are discussed. A detailed description of different mechanisms is presented to provide a comprehensive overview on the key parameters used in electric micropumps. Furthermore, electrode configurations and their shapes in different micropumps are explored and categorized to provide conclusive information for the selection of efficient, simple, and affordable strategies to transport fluids in microfluidic devices. In this paper, recent theoretical, numerical and experimental investigations are covered to provide a better insight both on the operational mechanisms and strategies for lab-on-chip applications.

Integrating image analysis with single cell RNA-seq data to study podocyte-specific changes in diabetic kidney disease.

Podocyte injury plays a crucial role in the progression of diabetic kidney disease (DKD). Injured podocytes demonstrate variations in nuclear shape and chromatin distribution. These morphometric changes have not yet been quantified in podocytes. Furthermore, the molecular mechanisms underlying these variations are poorly understood. Recent advances in omics have shed new lights into the biological mechanisms behind podocyte injury. However, there currently exists no study analyzing the biological mechanisms underlying podocyte morphometric variations during DKD. First, to study the importance of nuclear morphometrics, we performed morphometric quantification of podocyte nuclei from whole slide images of renal tissue sections obtained from murine models of DKD. Our results indicated that podocyte nuclear textural features demonstrate statistically significant difference in diabetic podocytes when compared to control. Additionally, the morphometric features demonstrated the existence of multiple subpopulations of podocytes suggesting a potential cause for their varying response to injury. Second, to study the underlying pathophysiology, we employed single cell RNA sequencing data from the murine models. Our results again indicated five subpopulations of podocytes in control and diabetic mouse models, validating the morphometrics-based results. Additionally, gene set enrichment analysis revealed epithelial to mesenchymal transition and apoptotic pathways in a subgroup of podocytes exclusive to diabetic mice, suggesting the molecular mechanism behind injury. Lastly, our results highlighted two distinct lineages of podocytes in control and diabetic cases suggesting a phenotypical change in podocytes during DKD. These results suggest that textural variations in podocyte nuclei may be key to understanding the pathophysiology behind podocyte injury.

FARCI: Fast and Robust Connectome Inference.

The inference of neuronal connectome from large-scale neuronal activity recordings, such as two-photon Calcium imaging, represents an active area of research in computational neuroscience. In this work, we developed FARCI (Fast and Robust Connectome Inference), a MATLAB package for neuronal connectome inference from high-dimensional two-photon Calcium fluorescence data. We employed partial correlations as a measure of the functional association strength between pairs of neurons to reconstruct a neuronal connectome. We demonstrated using in silico datasets from the Neural Connectomics Challenge (NCC) and those generated using the state-of-the-art simulator of Neural Anatomy and Optimal Microscopy (NAOMi) that FARCI provides an accurate connectome and its performance is robust to network sizes, missing neurons, and noise levels. Moreover, FARCI is computationally efficient and highly scalable to large networks. In comparison with the best performing connectome inference algorithm in the NCC, Generalized Transfer Entropy (GTE), and Fluorescence Single Neuron and Network Analysis Package (FluoroSNNAP), FARCI produces more accurate networks over different network sizes, while providing significantly better computational speed and scaling.