RESUMEN
Single-molecule Förster resonance energy transfer (smFRET) is a powerful technique for nanometer-scale studies of single molecules. Solution-based smFRET, in particular, can be used to study equilibrium intra- and intermolecular conformations, binding/unbinding events and conformational changes under biologically relevant conditions without ensemble averaging. However, single-spot smFRET measurements in solution are slow. Here, we detail a high-throughput smFRET approach that extends the traditional single-spot confocal geometry to a multispot one. The excitation spots are optically conjugated to two custom silicon single photon avalanche diode (SPAD) arrays. Two-color excitation is implemented using a periodic acceptor excitation (PAX), allowing distinguishing between singly- and doubly-labeled molecules. We demonstrate the ability of this setup to rapidly and accurately determine FRET efficiencies and population stoichiometries by pooling the data collected independently from the multiple spots. We also show how the high throughput of this approach can be used o increase the temporal resolution of single-molecule FRET population characterization from minutes to seconds. Combined with microfluidics, this high-throughput approach will enable simple real-time kinetic studies as well as powerful molecular screening applications.
Asunto(s)
ADN/química , Transferencia Resonante de Energía de Fluorescencia/métodos , Imagen Individual de Molécula/métodos , Simulación por Computador , ARN Polimerasas Dirigidas por ADN/química , Difusión , Ensayos Analíticos de Alto Rendimiento/métodos , Cinética , Láseres de Semiconductores , Microfluídica/métodos , Conformación Molecular , Fotones , Iniciación de la Transcripción GenéticaRESUMEN
Single-molecule Förster resonance energy transfer (smFRET) allows measuring distances between donor and acceptor fluorophores on the 3-10 nm range. Solution-based smFRET allows measurement of binding-unbinding events or conformational changes of dye-labeled biomolecules without ensemble averaging and free from surface perturbations. When employing dual (or multi) laser excitation, smFRET allows resolving the number of fluorescent labels on each molecule, greatly enhancing the ability to study heterogeneous samples. A major drawback to solution-based smFRET is the low throughput, which renders repetitive measurements expensive and hinders the ability to study kinetic phenomena in real-time. Here we demonstrate a high-throughput smFRET system that multiplexes acquisition by using 48 excitation spots and two 48-pixel single-photon avalanche diode array detectors. The system employs two excitation lasers allowing separation of species with one or two active fluorophores. The performance of the system is demonstrated on a set of doubly labeled double-stranded DNA oligonucleotides with different distances between donor and acceptor dyes along the DNA duplex. We show that the acquisition time for accurate subpopulation identification is reduced from several minutes to seconds, opening the way to high-throughput screening applications and real-time kinetics studies of enzymatic reactions such as DNA transcription by bacterial RNA polymerase.
Asunto(s)
ADN/química , Transferencia Resonante de Energía de Fluorescencia/métodos , Colorantes Fluorescentes/química , Cinética , Factores de TiempoRESUMEN
Animal affective computing is an emerging new field, which has so far mainly focused on pain, while other emotional states remain uncharted territories, especially in horses. This study is the first to develop AI models to automatically recognize horse emotional states from facial expressions using data collected in a controlled experiment. We explore two types of pipelines: a deep learning one which takes as input video footage, and a machine learning one which takes as input EquiFACS annotations. The former outperforms the latter, with 76% accuracy in separating between four emotional states: baseline, positive anticipation, disappointment and frustration. Anticipation and frustration were difficult to separate, with only 61% accuracy.
Asunto(s)
Emociones , Expresión Facial , Caballos/psicología , Animales , Emociones/fisiología , Aprendizaje Automático , Aprendizaje Profundo , Masculino , HumanosRESUMEN
Photon-HDF5 is an open-source and open file format for storing photon-counting data from single molecule microscopy experiments, introduced to simplify data exchange and increase the reproducibility of data analysis. Part of the Photon-HDF5 ecosystem, is phconvert, an extensible python library that allows converting proprietary formats into Photon-HDF5 files. However, its use requires some proficiency with command line instructions, the python programming language, and the YAML markup format. This creates a significant barrier for potential users without that expertise, but who want to benefit from the advantages of releasing their files in an open format. In this work, we present a GUI that lowers this barrier, thus simplifying the use of Photon-HDF5. This tool uses the phconvert python library to convert data files originally saved in proprietary data formats to Photon-HDF5 files, without users having to write a single line of code. Because reproducible analyses depend on essential experimental information, such as laser power or sample description, the GUI also includes (currently limited) functionality to associate valid metadata with the converted file, without having to write any YAML. Finally, the GUI includes several productivity-enhancing features such as whole-directory batch conversion and the ability to re-run a failed batch, only converting the files that could not be converted in the previous run.
RESUMEN
Single-molecule fluorescence spectroscopy (SMFS), based on the detection of individual molecules freely diffusing through the excitation spot of a confocal microscope, has allowed unprecedented insights into biological processes at the molecular level, but suffers from limited throughput. We have recently introduced a multispot version of SMFS, which allows achieving high-throughput SMFS by virtue of parallelization, and relies on custom silicon single-photon avalanche diode (SPAD) detector arrays. Here, we examine the premise of this parallelization approach, which is that data acquired from different spots is uncorrelated. In particular, we measure the optical crosstalk characteristics of the two 48-pixel SPAD arrays used in our recent SMFS studies, and demonstrate that it is negligible (crosstalk probability ≤ 1.1 10-3) and undetectable in cross-correlation analysis of actual single-molecule fluorescence data.
RESUMEN
Semiconductor quantum dots (QDs) have proven to be superior probes for single-molecule imaging compared to organic or genetically encoded fluorophores, but they are limited by difficulties in protein targeting, their larger size, and on-off blinking. Here, we report compact aqueous CdSe/CdS QDs with significantly improved bioconjugation efficiency and superior single-molecule optical properties. We have synthesized covalent protein labeling ligands (i.e., SNAP tags) that are optimized for nanoparticle use, and QDs functionalized with these ligands label SNAP-tagged proteins â¼10-fold more efficiently than existing SNAP ligands. Single-molecule analysis of these QDs shows 99% of time spent in the fluorescent on-state, â¼4-fold higher quantum efficiency than standard CdSe/ZnS QDs, and 350 million photons detected before photobleaching. Bright signals of these QDs enable us to track the stepping movement of a kinesin motor in vitro, and the improved labeling efficiency enables tracking of single kinesins in live cells.