Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics.

Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas brain activity has been studied at the micro- to meso-scale to reveal the connections between the dynamical patterns and the behaviors, investigations of neural population dynamics are mainly limited to single-scale analysis. Our goal is to develop a cross-scale dynamical model for the collective activity of neuronal populations. Here we introduce a bio-inspired deep learning approach, termed NeuroBondGraph Network (NBGNet), to capture cross-scale dynamics that can infer and map the neural data from multiple scales. Our model not only exhibits more than an 11-fold improvement in reconstruction accuracy, but also predicts synchronous neural activity and preserves correlated low-dimensional latent dynamics. We also show that the NBGNet robustly predicts held-out data across a long time scale (2 weeks) without retraining. We further validate the effective connectivity defined from our model by demonstrating that neural connectivity during motor behaviour agrees with the established neuroanatomical hierarchy of motor control in the literature. The NBGNet approach opens the door to revealing a comprehensive understanding of brain computation, where network mechanisms of multi-scale activity are critical.

Spatial resolution enhancement in photon-starved STED imaging using deep learning-based fluorescence lifetime analysis.

As a super-resolution imaging method, stimulated emission depletion (STED) microscopy has unraveled fine intracellular structures and provided insights into nanoscale organizations in cells. Although image resolution can be further enhanced by continuously increasing the STED-beam power, the resulting photodamage and phototoxicity are major issues for real-world applications of STED microscopy. Here we demonstrate that, with 50% less STED-beam power, the STED image resolution can be improved up to 1.45-fold using the separation of photons by a lifetime tuning (SPLIT) scheme combined with a deep learning-based phasor analysis algorithm termed flimGANE (fluorescence lifetime imaging based on a generative adversarial network). This work offers a new approach for STED imaging in situations where only a limited photon budget is available.

Generative adversarial network enables rapid and robust fluorescence lifetime image analysis in live cells.

Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study molecular states in complex cellular environment as the lifetime readings are not biased by fluorophore concentration or excitation power. However, the current methods to generate FLIM images are either computationally intensive or unreliable when the number of photons acquired at each pixel is low. Here we introduce a new deep learning-based method termed flimGANE (fluorescence lifetime imaging based on Generative Adversarial Network Estimation) that can rapidly generate accurate and high-quality FLIM images even in the photon-starved conditions. We demonstrated our model is up to 2,800 times faster than the gold standard time-domain maximum likelihood estimation (TD_MLE) and that flimGANE provides a more accurate analysis of low-photon-count histograms in barcode identification, cellular structure visualization, Förster resonance energy transfer characterization, and metabolic state analysis in live cells. With its advantages in speed and reliability, flimGANE is particularly useful in fundamental biological research and clinical applications, where high-speed analysis is critical.

Recent developments in the characterization of nucleic acid hybridization kinetics.

Hybridization of nucleic acids (NAs) is a fundamental molecular mechanism that drives many cellular processes and enables new biotechnologies as well as therapeutics. However, existing methods that measure hybridization kinetics of nucleic acids are either performed at the ensemble level or constrained to non-native physiological conditions. Recent advances in 3D single-molecule tracking techniques break these limitations by allowing multiple annealing and melting events to be observed on a single oligonucleotide freely diffusing inside a live mammalian cell. This review provides an overview of diverse approaches to measuring NA hybridization kinetics at the single-molecule level and in live cells, and concludes with a synopsis of unresolved challenges and opportunities in the live-cell hybridization kinetics measurements. Important discoveries made by NA kinetics measurements and biotechnologies that can be improved with a deeper understanding of hybridization kinetics are also described.

Measuring DNA Hybridization Kinetics in Live Cells Using a Time-Resolved 3D Single-Molecule Tracking Method.

Single-molecule detection enables direct characterization of annealing/melting kinetics of nucleic acids without the need for synchronization of molecular states, but the current experiments are not carried out in a native cellular context. Here we describe an integrated 3D single-molecule tracking and lifetime measurement method that can follow individual DNA molecules diffusing inside a mammalian cell and observe multiple annealing and melting events on the same molecules. By comparing the hybridization kinetics of the same DNA strand in vitro, we found the association constants can be 13- to 163-fold higher in the molecular crowding cellular environment.