In vivo long-term investigation of tumor bearing mKate2 by an in-house fluorescence molecular imaging system

The fluorescence mKate2 labeling as a visualizing system for monitoring small extracellular vesicles

Small extracellular vesicles (sEVs) are nanosized vesicles enclosed in a lipid membrane released by nearly all cell types. sEVs have been considered as reliable biomarkers for diagnostics and effective carriers. Despite the clear importance of sEV functionality, sEV research faces challenges imposed by the small size and precise imaging of sEVs. Recent advances in live and high-resolution microscopy, combined with efficient labeling strategies, enable us to investigate the composition and behavior of EVs within living organisms. Here, a modified sEVs was generated with a near infrared fluorescence protein mKate2 using a VSVG viral pseudotyping-based approach for monitoring sEVs. An observed was made that the mKate2-tagged protein can be incorporated into the membranes of sEVs without altering their physical properties. In vivo imaging demonstrates that sEVs labeled with mKate2 exhibit excellent brightness and high photostability, allowing the acquisition of long-term investigation comparable to those achieved with mCherry labeling. Importantly, the mKate2-tagged sEVs show a low toxicity and exhibit a favorable safety profile. Furthermore, the co-expression of mKate2 and rabies virus glycoprotein (RVG) peptide on sEVs enables brain-targeted visualization, suggesting the mKate2 tag does not alter the biodistribution of sEVs. Together, the study presents the mKate2 tag as an efficient tracker for sEVs to monitor tissue-targeting and biodistribution in vivo.

Performance enhancement of diffuse fluorescence tomography based on an extended Kalman filtering-long short term memory neural network correction model

To alleviate the ill-posedness of diffuse fluorescence tomography (DFT) reconstruction and improve imaging quality and speed, a model-derived deep-learning method is proposed by combining extended Kalman filtering (EKF) with a long short term memory (LSTM) neural network, where the iterative process parameters acquired by implementing semi-iteration EKF (SEKF) served as inputs to the LSTM neural network correction model for predicting the optimal fluorescence distributions. To verify the effectiveness of the SEKF-LSTM algorithm, a series of numerical simulations, phantom and in vivo experiments are conducted, and the experimental results are quantitatively evaluated and compared with the traditional EKF algorithm. The simulation experimental results show that the proposed new algorithm can effectively improve the reconstructed image quality and reconstruction speed. Importantly, the LSTM correction model trained by the simulation data also obtains satisfactory results in the experimental data, suggesting that the SEKF-LSTM algorithm possesses strong generalization ability and great potential for practical applications.

Encoder-decoder deep learning network for simultaneous reconstruction of fluorescence yield and lifetime distributions

A time-domain fluorescence molecular tomography in reflective geometry (TD-rFMT) has been proposed to circumvent the penetration limit and reconstruct fluorescence distribution within a 2.5-cm depth regardless of the object size. In this paper, an end-to-end encoder-decoder network is proposed to further enhance the reconstruction performance of TD-rFMT. The network reconstructs both the fluorescence yield and lifetime distributions directly from the time-resolved fluorescent signals. According to the properties of TD-rFMT, proper noise was added to the simulation training data and a customized loss function was adopted for self-supervised and supervised joint training. Simulations and phantom experiments demonstrate that the proposed network can significantly improve the spatial resolution, positioning accuracy, and accuracy of lifetime values.

A genetic model for in vivo proximity labelling of the mammalian secretome

Organ functions are highly specialized and interdependent. Secreted factors regulate organ development and mediate homeostasis through serum trafficking and inter-organ communication. Enzyme-catalysed proximity labelling enables the identification of proteins within a specific cellular compartment. Here, we report a BirA*G3 mouse strain that enables CRE-dependent promiscuous biotinylation of proteins trafficking through the endoplasmic reticulum. When broadly activated throughout the mouse, widespread labelling of proteins was observed within the secretory pathway. Streptavidin affinity purification and peptide mapping by quantitative mass spectrometry (MS) proteomics revealed organ-specific secretory profiles and serum trafficking. As expected, secretory proteomes were highly enriched for signal peptide-containing proteins, highlighting both conventional and non-conventional secretory processes, and ectodomain shedding. Lower-abundance proteins with hormone-like properties were recovered and validated using orthogonal approaches. Hepatocyte-specific activation of BirA*G3 highlighted liver-specific biotinylated secretome profiles. The BirA*G3 mouse model demonstrates enhanced labelling efficiency and tissue specificity over viral transduction approaches and will facilitate a deeper understanding of secretory protein interplay in development, and in healthy and diseased adult states.

Depth-recognizable time-domain fluorescence molecular tomography in reflective geometry

Conventional fluorescence molecular tomography (FMT) reconstruction requires photons penetrating the whole object, which limits its applications to small animals. However, by utilizing reflective photons, fluorescence distribution near the surface could be reconstructed regardless of the object size, which may extend the applications of FMT to surgical navigation and so on. Therefore, time-domain reflective fluorescence molecular tomography (TD-rFMT) is proposed in this paper. The system excites and detects the emission light from the same angle within a field of view of 5 cm. Because the detected intensities of targets depend strongly on the depth, the reconstruction of targets in deep regions would be evidently affected. Therefore, a fluorescence yield reconstruction method with depth regularization and a weighted separation reconstruction strategy for lifetime are developed to enhance the performance for deep targets. Through simulations and phantom experiments, TD-rFMT is proved capable of reconstructing fluorescence distribution within a 2.5-cm depth with accurate reconstructed yield, lifetime, and target position(s).