No abstract

Fluorescence lifetime imaging (FLI) provides unique quantitative information in biomedical and molecular biology studies but relies on complex data-fitting techniques to derive the quantities of interest. Herein, we propose a fit-free approach in FLI image formation that is based on deep learning (DL) to quantify fluorescence decays simultaneously over a whole image and at fast speeds. We report on a deep neural network (DNN) architecture, named fluorescence lifetime imaging network (FLI-Net) that is designed and trained for different classes of experiments, including visible FLI and near-infrared (NIR) FLI microscopy (FLIM) and NIR gated macroscopy FLI (MFLI). FLI-Net outputs quantitatively the spatially resolved lifetime-based parameters that are typically employed in the field. We validate the utility of the FLI-Net framework by performing quantitative microscopic and preclinical lifetime-based studies across the visible and NIR spectra, as well as across the 2 main data acquisition technologies. These results demonstrate that FLI-Net is well suited to accurately quantify complex fluorescence lifetimes in cells and, in real time, in intact animals without any parameter settings. Hence, FLI-Net paves the way to reproducible and quantitative lifetime studies at unprecedented speeds, for improved dissemination and impact of FLI in many important biomedical applications ranging from fundamental discoveries in molecular and cellular biology to clinical translation.

Spectrally resolved fluorescence lifetime imaging1–3 and spatial multiplexing1,4,5 have offered information content and collection-efficiency boosts in microscopy, but efficient implementations for macroscopic applications are still lacking. An imaging platform based on time-resolved structured light and hyperspectral single-pixel detection has been developed to perform quantitative macroscopic fluorescence lifetime imaging (MFLI) over a large field of view (FOV) and multiple spectral bands simultaneously. The system makes use of three digital micromirror device (DMD)-based spatial light modulators (SLMs) to generate spatial optical bases and reconstruct N by N images over 16 spectral channels with a time-resolved capability (~40 ps temporal resolution) using fewer than N2 optical measurements. We demonstrate the potential of this new imaging platform by quantitatively imaging near-infrared (NIR) Förster resonance energy transfer (FRET) both in vitro and in vivo. The technique is well suited for quantitative hyperspectral lifetime imaging with a high sensitivity and paves the way for many important biomedical applications.

Perturbation Monte Carlo (pMC) has been previously proposed to rapidly recompute optical measurements when small perturbations of optical properties are considered, but it was largely restricted to changes associated with prior tissue segments or regions-of-interest. In this work, we expand pMC to compute spatially and temporally resolved sensitivity profiles, i.e. the Jacobians, for diffuse optical tomography (DOT) applications. By recording the pseudo random number generator (PRNG) seeds of each detected photon, we are able to "replay" all detected photons to directly create the 3D sensitivity profiles for both absorption and scattering coefficients. We validate the replay-based Jacobians against the traditional adjoint Monte Carlo (aMC) method, and demonstrate the feasibility of using this approach for efficient 3D image reconstructions using in vitro hyperspectral wide-field DOT measurements. The strengths and limitations of the replay approach regarding its computational efficiency and accuracy are discussed, in comparison with aMC, for point-detector systems as well as wide-field pattern-based and hyperspectral imaging systems. The replay approach has been implemented in both of our open-source MC simulators-MCX and MMC (http://mcx.space)

Macroscopic fluorescence lifetime imaging (MFLI) via compressed sensed (CS) measurements enables efficient and accurate quantification of molecular interactions in vivo over a large field of view (FOV). However, the current data-processing workflow is slow, complex and performs poorly under photon-starved conditions. In this paper, we propose Net-FLICS, a novel image reconstruction method based on a convolutional neural network (CNN), to directly reconstruct the intensity and lifetime images from raw time-resolved CS data. By carefully designing a large simulated dataset, Net-FLICS is successfully trained and achieves outstanding reconstruction performance on both in vitro and in vivo experimental data and even superior results at low photon count levels for lifetime quantification.

The giant panda (Ailuropoda melanoleuca) and red panda (Ailurus fulgens), two obligate bamboo feeders, have distinct phylogenetic positions in the order Carnivora. Bamboo is extraordinarily rich in plant secondary metabolites, such as allied phenolic and polyphenolic compounds and even toxic cyanide compounds. Here, the enrichment of putative cyanide-digesting gut microbes, in combination with adaptations related to morphology (e.g., pseudothumbs) and genomic signatures, show that the giant panda and red panda have evolved some common traits to adapt to their bamboo diet. Thus, here is another story of diet-driven gut microbiota in nature.

Establishing an accurate evolutionary timescale for green plants (Viridiplantae) is essential to understanding their interaction and coevolution with the Earth’s climate and the many organisms that rely on green plants. Despite being the focus of numerous studies, the timing of the origin of green plants and the divergence of major clades within this group remain highly controversial. Here, we infer the evolutionary timescale of green plants by analyzing 81 protein-coding genes from 99 chloroplast genomes, using a core set of 21 fossil calibrations. We test the sensitivity of our divergence-time estimates to various components of Bayesian molecular dating, including the tree topology, clock models, clock-partitioning schemes, rate priors, and fossil calibrations. We find that the choice of clock model affects date estimation and that the independent-rates model provides a better fit to the data than the autocorrelated-rates model. Varying the rate prior and tree topology had little impact on age estimates, with far greater differences observed among calibration choices and clock-partitioning schemes. Our analyses yield date estimates ranging from the Paleoproterozoic to Mesoproterozoic for crown-group green plants, and from the Ediacaran to Middle Ordovician for crown-group land plants. We present divergence-time estimates of the major groups of green plants that take into account various sources of uncertainty. Our proposed timeline lays the foundation for further investigations into how green plants shaped the global climate and ecosystems, and how embryophytes became dominant in terrestrial environments.

Monte Carlo methods are commonly used as the gold standard in modeling photon transport through turbid media. With the rapid development of structured light applications, an accurate and efficient method capable of simulating arbitrary illumination patterns and complex detection schemes over large surface area is in great need. Here we report a generalized mesh-based Monte Carlo algorithm to support a variety of wide-field illumination methods, including spatial-frequency-domain imaging (SFDI) patterns and arbitrary 2-D patterns. The extended algorithm can also model wide-field detectors such as a free-space CCD camera. The significantly enhanced flexibility of source and detector modeling is achieved via a fast mesh retessellation process that combines the target domain and the source/detector space in a single tetrahedral mesh. Both simulations of complex domains and comparisons with phantom measurements are included to demonstrate the flexibility, efficiency and accuracy of the extended algorithm. Our updated open-source software is provided at http://mcx.space/mmc.