Multicolor fluorescent imaging by space-constrained computational spectral imaging

Single-shot multi-color fluorescence microscopy via a colorimetry camera

Multi-color fluorescence microscopy presents highly detailed biological samples interactively. However, current multi-color methods suffer from an intricate optical setup, complicated image analysis, or a long acquisition time. To address these issues, here we develop a simple multi-color method based on a customized colorimetry camera to enable the detection of multiple structures from single-shot acquisition. The unfiltered channel (W pixels) and color channels (R, G, B, and NIR pixels) in this customized camera simultaneously provide a broad detection wavelength range and high detection sensitivity. We built a simple optical setup by replacing the monochrome camera in a basic fluorescence microscopy system with a colorimetry camera, and developed effective image analysis procedures to reconstruct a multi-color image from a single frame of a raw image. We demonstrated single-shot four-color wide-field fluorescence imaging on fixed cos-7 cells with < 5% cross talk, which is comparable to the best reported values. Our method greatly simplifies both the optical system and image analysis in the widely used method of multi-color fluorescence microscopy, thus offering an effective and easy way to study multiple objects at the same time.

Live-cell fluorescence spectral imaging as a data science challenge

Live-cell fluorescence spectral imaging is an evolving modality of microscopy that uses specific properties of fluorophores, such as excitation or emission spectra, to detect multiple molecules and structures in intact cells. The main challenge of analyzing live-cell fluorescence spectral imaging data is the precise quantification of fluorescent molecules despite the weak signals and high noise found when imaging living cells under non-phototoxic conditions. Beyond the optimization of fluorophores and microscopy setups, quantifying multiple fluorophores requires algorithms that separate or unmix the contributions of the numerous fluorescent signals recorded at the single pixel level. This review aims to provide both the experimental scientist and the data analyst with a straightforward description of the evolution of spectral unmixing algorithms for fluorescence live-cell imaging. We show how the initial systems of linear equations used to determine the concentration of fluorophores in a pixel progressively evolved into matrix factorization, clustering, and deep learning approaches. We outline potential future trends on combining fluorescence spectral imaging with label-free detection methods, fluorescence lifetime imaging, and deep learning image analysis.

Compressed sensing in fluorescence microscopy

Compressed sensing (CS) is a signal processing approach that solves ill-posed inverse problems, from under-sampled data with respect to the Nyquist criterium. CS exploits sparsity constraints based on the knowledge of prior information, relative to the structure of the object in the spatial or other domains. It is commonly used in image and video compression as well as in scientific and medical applications, including computed tomography and magnetic resonance imaging. In the field of fluorescence microscopy, it has been demonstrated to be valuable for fast and high-resolution imaging, from single-molecule localization, super-resolution to light-sheet microscopy. Furthermore, CS has found remarkable applications in the field of mesoscopic imaging, facilitating the study of small animals' organs and entire organisms. This review article illustrates the working principles of CS, its implementations in optical imaging and discusses several relevant uses of CS in the field of fluorescence imaging from super-resolution microscopy to mesoscopy.

Excitation spectral microscopy for highly multiplexed fluorescence imaging and quantitative biosensing

The multiplexing capability of fluorescence microscopy is severely limited by the broad fluorescence spectral width. Spectral imaging offers potential solutions, yet typical approaches to disperse the local emission spectra notably impede the attainable throughput. Here we show that using a single, fixed fluorescence emission detection band, through frame-synchronized fast scanning of the excitation wavelength from a white lamp via an acousto-optic tunable filter, up to six subcellular targets, labeled by common fluorophores of substantial spectral overlap, can be simultaneously imaged in live cells with low (~1%) crosstalks and high temporal resolutions (down to ~10 ms). The demonstrated capability to quantify the abundances of different fluorophores in the same sample through unmixing the excitation spectra next enables us to devise novel, quantitative imaging schemes for both bi-state and Förster resonance energy transfer fluorescent biosensors in live cells. We thus achieve high sensitivities and spatiotemporal resolutions in quantifying the mitochondrial matrix pH and intracellular macromolecular crowding, and further demonstrate, for the first time, the multiplexing of absolute pH imaging with three additional target organelles/proteins to elucidate the complex, Parkin-mediated mitophagy pathway. Together, excitation spectral microscopy provides exceptional opportunities for highly multiplexed fluorescence imaging. The prospect of acquiring fast spectral images without the need for fluorescence dispersion or care for the spectral response of the detector offers tremendous potential.

Simultaneous multi-color optical sectioning fluorescence microscopy with wavelength-coded volume holographic gratings

Optical sectioning fluorescence microscopy provides high contrast images of volumetric samples and has been widely used for many biological applications. However, simultaneously acquiring multi-color fluorescence images require additional optical elements and devices, which are bulky, wavelength specific, and not cost-effective. In this paper, wavelength-coded volume holographic gratings (WC-VHGs) based optical sectioning fluorescence microscopy is proposed to simultaneously offer multi-color fluorescence images with fine out-of-focus background rejection. Due to wavelength degeneracy, multiplexed WC-VHGs are capable of acquiring multi-wavelength fluorescence images in a single shot, and displaying the laterally separated multi-wavelength images onto CCD. In our system optical sectioning capability is achieved through speckle illumination and HiLo imaging method. To demonstrate imaging characteristics of our system, dual-wavelength fluorescence images of both standard fluorescent microspheres and ex vivo mT/mG mice cardiac tissue are presented. Current results may find important applications in hyperspectral imaging for biomedical research.

Quasi-spectral characterization of intracellular regions in bright-field light microscopy images

Investigation of cell structure is hardly imaginable without bright-field microscopy. Numerous modifications such as depth-wise scanning or videoenhancement make this method being state-of-the-art. This raises a question what maximal information can be extracted from ordinary (but well acquired) bright-field images in a model-free way. Here we introduce a method of a physically correct extraction of features for each pixel when these features resemble a transparency spectrum. The method is compatible with existent ordinary bright-field microscopes and requires mathematically sophisticated data processing. Unsupervised clustering of the spectra yields reasonable semantic segmentation of unstained living cells without any a priori information about their structures. Despite the lack of reference data (to prove strictly that the proposed feature vectors coincide with transparency), we believe that this method is the right approach to an intracellular (semi)quantitative and qualitative chemical analysis.