Multispectral fingerprinting for improved in vivo cell dynamics analysis

Background: Tracing cell dynamics in the embryo becomes tremendously difficult when cell trajectories cross in space and time and tissue density obscure individual cell borders. Here, we used the chick neural crest (NC) as a model to test multicolor cell labeling and multispectral confocal imaging strategies to overcome these roadblocks. Results: We found that multicolor nuclear cell labeling and multispectral imaging led to improved resolution of in vivo NC cell identification by providing a unique spectral identity for each cell. NC cell spectral identity allowed for more accurate cell tracking and was consistent during short term time-lapse imaging sessions. Computer model simulations predicted significantly better object counting for increasing cell densities in 3-color compared to 1-color nuclear cell labeling. To better resolve cell contacts, we show that a combination of 2-color membrane and 1-color nuclear cell labeling dramatically improved the semi-automated analysis of NC cell interactions, yet preserved the ability to track cell movements. We also found channel versus lambda scanning of multicolor labeled embryos significantly reduced the time and effort of image acquisition and analysis of large 3D volume data sets. Conclusions: Our results reveal that multicolor cell labeling and multispectral imaging provide a cellular fingerprint that may uniquely determine a cell's position within the embryo. Together, these methods offer a spectral toolbox to resolve in vivo cell dynamics in unprecedented detail

Eisosomes are dynamic plasma membrane domains showing Pil1-Lsp1 heteroligomer binding equilibrium

Eisosomes are plasma membrane domains concentrating lipids, transporters, and signaling molecules. In the budding yeast Saccharomyces cerevisiae, these domains are structured by scaffolds composed mainly by two cytoplasmic proteins Pil1 and Lsp1. Eisosomes are immobile domains, have relatively uniform size, and encompass thousands of units of the core proteins Pil1 and Lsp1. In this work we used fluorescence fluctuation analytical methods to determine the dynamics of eisosome core proteins at different subcellular locations. Using a combination of scanning techniques with autocorrelation analysis, we show that Pil1 and Lsp1 cytoplasmic pools freely diffuse whereas an eisosome-associated fraction of these proteins exhibits slow dynamics that fit with a binding-unbinding equilibrium. Number and brightness analysis shows that the eisosome-associated fraction is oligomeric, while cytoplasmic pools have lower aggregation states. Fluorescence lifetime imaging results indicate that Pil1 and Lsp1 directly interact in the cytoplasm and within the eisosomes. These results support a model where Pil1-Lsp1 heterodimers are the minimal eisosomes building blocks. Moreover, individual-eisosome fluorescence fluctuation analysis shows that eisosomes in the same cell are not equal domains: while roughly half of them are mostly static, the other half is actively exchanging core protein subunits.Fil: Olivera Couto, Agustina. Instituto Pasteur de Montevideo; UruguayFil: Salzman, Valentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); Argentina. Instituto Pasteur de Montevideo; UruguayFil: Mailhos, Milagros. Instituto Pasteur de Montevideo; UruguayFil: Digman, Michelle A.. University of California at Irvine; Estados Unidos. University of New England; AustraliaFil: Gratton, Enrico. University of California at Irvine; Estados UnidosFil: Aguilar, Pablo Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); Argentina. Instituto Pasteur de Montevideo; Urugua

Open Source Software for Automatic Detection of Cone Photoreceptors in Adaptive Optics Ophthalmoscopy Using Convolutional Neural Networks

Imaging with an adaptive optics scanning light ophthalmoscope (AOSLO) enables direct visualization of the cone photoreceptor mosaic in the living human retina. Quantitative analysis of AOSLO images typically requires manual grading, which is time consuming, and subjective; thus, automated algorithms are highly desirable. Previously developed automated methods are often reliant on ad hoc rules that may not be transferable between different imaging modalities or retinal locations. In this work, we present a convolutional neural network (CNN) based method for cone detection that learns features of interest directly from training data. This cone-identifying algorithm was trained and validated on separate data sets of confocal and split detector AOSLO images with results showing performance that closely mimics the gold standard manual process. Further, without any need for algorithmic modifications for a specific AOSLO imaging system, our fully-automated multi-modality CNN-based cone detection method resulted in comparable results to previous automatic cone segmentation methods which utilized ad hoc rules for different applications. We have made free open-source software for the proposed method and the corresponding training and testing datasets available online

Wide-Field Multi-Parameter FLIM: Long-Term Minimal Invasive Observation of Proteins in Living Cells.

Time-domain Fluorescence Lifetime Imaging Microscopy (FLIM) is a remarkable tool to monitor the dynamics of fluorophore-tagged protein domains inside living cells. We propose a Wide-Field Multi-Parameter FLIM method (WFMP-FLIM) aimed to monitor continuously living cells under minimum light intensity at a given illumination energy dose. A powerful data analysis technique applied to the WFMP-FLIM data sets allows to optimize the estimation accuracy of physical parameters at very low fluorescence signal levels approaching the lower bound theoretical limit. We demonstrate the efficiency of WFMP-FLIM by presenting two independent and relevant long-term experiments in cell biology: 1) FRET analysis of simultaneously recorded donor and acceptor fluorescence in living HeLa cells and 2) tracking of mitochondrial transport combined with fluorescence lifetime analysis in neuronal processes

A general method to quantify ligand-driven oligomerization from fluorescence-based images

Here, we introduce fluorescence intensity fluctuation spectrometry for determining the identity, abundance and stability of protein oligomers. This approach was tested on monomers and oligomers of known sizes and was used to uncover the oligomeric states of the epidermal growth factor receptor and the secretin receptor in the presence and absence of their agonist ligands. This method is fast and is scalable for high-throughput screening of drugs targeting protein–protein interactions

Quantitative Analysis Techniques for Assessing Organelle Organization and Dynamics in Individual Cells

In biomedical optics and microscopy, the organization and morphology of organelles have been widely studied. In spite of novel imaging techniques, there is still a lack of quantitative tools to easily measure cellular characteristics from image data. Previous studies have explored multiple approaches to assess organelle organization and alignment, resulting in complicated and extensive algorithms that are both subject to multiple steps of image processing and influenced by non-cellular artifacts. In this thesis, a technique called the Modified Blanket Method (MBM) is introduced to quantify organelle organization through measurements of fractal dimension (FD) on a pixel-by-pixel basis. With the use of simulated fractal clouds, it is demonstrated that the MBM is capable of accurately and rapidly quantify FD, having a higher sensitivity to a wider range of FD values compared to previous methods. Furthermore, the MBM could differentiate mitochondrial organization of radiation-resistant A549 lung cancer cells at different time points post-radiation. In later experiments, the MBM is combined with similar computational techniques to quantify fiber alignment and nuclear shape through measurements of directional variance (DV) and nuclear aspect ratio (NAR). The simultaneous use of these tools demonstrated that the organization and alignment of mitochondria and actin of NIH 3T3 cells treated with L-buthionine-sulfoximine (BSO) change over time, having different nuclear shapes as well. It is then concluded the this set of computational tools is capable of providing significant cellular data, which could potentially be employed to understand the cellular dynamics of multiple pathological conditions such as diabetes, Alzheimer’s, Leigh’s syndrome, and myopathy, all of which are known to be influenced by dysfunctional organelles

Quantitative Imaging in Electron and Confocal Microscopies for Applications in Biology

Among the large number of topics related to the quantification of images in electron and confocal microscopies for applications in biology, we selected four subjects that we consider to be representative of some recent tendencies. The first is the quantification of three-dimensional data sets recorded routinely in scanning confocal microscopy. The second is the quantification of the textural and fractal appearance of images. The two other topics are related to image series, which are more and more often provided by imaging instruments. The first kind of series concerns electron energy-filtered images. We show that the parametric (modelling) approach can be complemented by non-parametric approaches (e.g., different variants of multivariate statistical techniques). The other kind of series consists of multiple mappings of a specimen. We describe several new tools for the study and quantification of the co-location, with potential application to multiple mappings in microanalysis or in fluorescence microscopy