scholarly journals Spatial transcriptomics using combinatorial fluorescence spectral and lifetime encoding, imaging and analysis

2021 ◽  
Author(s):  
Tam Vu ◽  
Alexander Vallmitjana ◽  
Joshua Gu ◽  
Kieu La ◽  
Qi Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Error Correction ◽  
Basic Research ◽  
Spatial Context ◽  
Fluorescence Lifetime Imaging ◽  
Sequencing Data ◽  
Time Dimension ◽  
Time Resolved ◽  
Ffpe Tissues

Multiplexed mRNA profiling in the spatial context provides important new information enabling basic research and clinical applications. Unfortunately, most existing spatial transcriptomics methods are limited due to either low multiplexing or assay complexity. Here, we introduce a new spatialomics technology, termed Multi Omic Single-scan Assay with Integrated Combinatorial Analysis (MOSAICA), that integrates in situ labeling of mRNA markers in cells or tissues with combinatorial fluorescence spectral and lifetime encoded probes, spectral and time-resolved fluorescence imaging, and machine learning-based target decoding. This technology is the first application combining the biophotonic techniques; Spectral and Fluorescence Lifetime Imaging and Microscopy (FLIM), to the field of spatial transcriptomics. By integrating the time dimension with conventional spectrum-based measurements, MOSAICA enables direct, highly-multiplexed, in situ spatial biomarker profiling in a single round of staining and imaging while providing error correction removal of background autofluorescence. We demonstrate mRNA encoding using combinatorial spectral and lifetime labeling and target decoding and quantification using a phasor-based image segmentation and machine learning clustering technique. We then showcase MOSAICA′s multiplexing scalability in detecting 10-plex targets in fixed colorectal cancer cells using combinatorial labeling of only five fluorophores with facile error-correction and removal of autofluorescent moieties. MOSAICA′s analysis is strongly correlated with sequencing data (Pearson′s r = 0.9) and was further benchmarked using RNAscope™ and LGC Stellaris™. We further apply MOSAICA for multiplexed analysis of clinical melanoma Formalin-Fixed Paraffin-Embedded (FFPE) tissues that have a high degree of tissue scattering and intrinsic autofluorescence, demonstrating the robustness of the approach. MOSAICA represents a simple, versatile, and scalable tool for targeted spatial transcriptomics analysis that can find broad utility in constructing human cell atlases, elucidating biological and disease processes in the spatial context, and serving as companion diagnostics for stratified patient care.

scholarly journals Spatial transcriptomics using combinatorial fluorescence spectral and lifetime encoding, imaging and analysis

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Tam Vu ◽  
Alexander Vallmitjana ◽  
Joshua Gu ◽  
Kieu La ◽  
Qi Xu ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Error Detection ◽  
Basic Research ◽  
Protein Markers ◽  
Sequencing Data ◽  
Time Resolved ◽  
New Information ◽  
Formalin Fixed Paraffin ◽  
Formalin Fixed Paraffin Embedded ◽  
Ffpe Tissues

AbstractMultiplexed mRNA profiling in the spatial context provides new information enabling basic research and clinical applications. Unfortunately, existing spatial transcriptomics methods are limited due to either low multiplexing or complexity. Here, we introduce a spatialomics technology, termed Multi Omic Single-scan Assay with Integrated Combinatorial Analysis (MOSAICA), that integrates in situ labeling of mRNA and protein markers in cells or tissues with combinatorial fluorescence spectral and lifetime encoded probes, spectral and time-resolved fluorescence imaging, and machine learning-based decoding. We demonstrate MOSAICA’s multiplexing scalability in detecting 10-plex targets in fixed colorectal cancer cells using combinatorial labeling of five fluorophores with facile error-detection and removal of autofluorescence. MOSAICA’s analysis is strongly correlated with sequencing data (Pearson’s r = 0.96) and was further benchmarked using RNAscopeTM and LGC StellarisTM. We further apply MOSAICA for multiplexed analysis of clinical melanoma Formalin-Fixed Paraffin-Embedded (FFPE) tissues. We finally demonstrate simultaneous co-detection of protein and mRNA in cancer cells.

scholarly journals CNV-P: a machine-learning framework for predicting high confident copy number variations

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12564
Author(s):  
Taifu Wang ◽  
Jinghua Sun ◽  
Xiuqing Zhang ◽  
Wen-Jing Wang ◽  
Qing Zhou
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
Copy Number ◽  
Genetic Disorders ◽  
Genetic Diseases ◽  
Basic Research ◽  
Read Depth ◽  
Copy Number Variations ◽  
Sequencing Data ◽  
Learning Framework

Background Copy-number variants (CNVs) have been recognized as one of the major causes of genetic disorders. Reliable detection of CNVs from genome sequencing data has been a strong demand for disease research. However, current software for detecting CNVs has high false-positive rates, which needs further improvement. Methods Here, we proposed a novel and post-processing approach for CNVs prediction (CNV-P), a machine-learning framework that could efficiently remove false-positive fragments from results of CNVs detecting tools. A series of CNVs signals such as read depth (RD), split reads (SR) and read pair (RP) around the putative CNV fragments were defined as features to train a classifier. Results The prediction results on several real biological datasets showed that our models could accurately classify the CNVs at over 90% precision rate and 85% recall rate, which greatly improves the performance of state-of-the-art algorithms. Furthermore, our results indicate that CNV-P is robust to different sizes of CNVs and the platforms of sequencing. Conclusions Our framework for classifying high-confident CNVs could improve both basic research and clinical diagnosis of genetic diseases.

scholarly journals Droplet Microfluidics for Food and Nutrition Applications

Micromachines ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 863
Author(s):  
Karin Schroen ◽  
Claire Berton-Carabin ◽  
Denis Renard ◽  
Mélanie Marquis ◽  
Adeline Boire ◽  
...  
Keyword(s):  
Applied Sciences ◽  
Basic Research ◽  
Droplet Microfluidics ◽  
Time Resolved ◽  
Lab On Chip ◽  
Food And Nutrition ◽  
Research Questions ◽  
On Chip ◽  
Nutritional Sciences

Droplet microfluidics revolutionizes the way experiments and analyses are conducted in many fields of science, based on decades of basic research. Applied sciences are also impacted, opening new perspectives on how we look at complex matter. In particular, food and nutritional sciences still have many research questions unsolved, and conventional laboratory methods are not always suitable to answer them. In this review, we present how microfluidics have been used in these fields to produce and investigate various droplet-based systems, namely simple and double emulsions, microgels, microparticles, and microcapsules with food-grade compositions. We show that droplet microfluidic devices enable unprecedented control over their production and properties, and can be integrated in lab-on-chip platforms for in situ and time-resolved analyses. This approach is illustrated for on-chip measurements of droplet interfacial properties, droplet–droplet coalescence, phase behavior of biopolymer mixtures, and reaction kinetics related to food digestion and nutrient absorption. As a perspective, we present promising developments in the adjacent fields of biochemistry and microbiology, as well as advanced microfluidics–analytical instrument coupling, all of which could be applied to solve research questions at the interface of food and nutritional sciences.

Exciton dynamics in thick GaN MOVPE epilayers deposited on sapphire.

1997 ◽  
Vol 2 ◽  
Author(s):  
J. Allègre ◽  
P. Lefebvre ◽  
J. Camassel ◽  
B. Beaumont ◽  
Pierre Gibart
Keyword(s):  
Layer Thickness ◽  
Buffer Layers ◽  
Rate Equations ◽  
Time Resolved ◽  
Versus Layer ◽  
Level Model ◽  
Shallow Impurities ◽  
Aln Buffer ◽  
Time Resolved Photoluminescence

Time-resolved photoluminescence spectra have been recorded on three GaN epitaxial layers of thickness 2.5 μm, 7 μm and 16 μm, at various temperatures ranging from 8K to 300K. The layers were deposited by MOVPE on (0001) sapphire substrates with standard AlN buffer layers. To achieve good homogeneities, the growth was in-situ monitored by laser reflectometry. All GaN layers showed sharp excitonic peaks in cw PL and three excitonic contributions were seen by reflectivity. The recombination dynamics of excitons depends strongly upon the layer thickness. For the thinnest layer, exponential decays with τ ~ 35 ps have been measured for both XA and XB free excitons. For the thickest layer, the decay becomes biexponential with τ1 ~ 80 ps and τ2 ~ 250 ps. These values are preserved up to room temperature. By solving coupled rate equations in a four-level model, this evolution is interpreted in terms of the reduction of density of both shallow impurities and deep traps, versus layer thickness, roughly following a L−1 law.

Monitoring Polymer-Assisted Mechanochemical Cocrystallisation Through in Situ X-Ray Powder Diffraction

2020 ◽  
Author(s):  
Luzia S. Germann ◽  
Sebastian T. Emmerling ◽  
Manuel Wilke ◽  
Robert E. Dinnebier ◽  
Mariarosa Moneghini ◽  
...  
Keyword(s):  
Molecular Weight ◽  
Powder Diffraction ◽  
Reaction Rate ◽  
Polymer Additives ◽  
Time Resolved ◽  
X Ray

Time-resolved mechanochemical cocrystallisation studies have so-far focused solely on neat and liquid-assisted grinding. Here, we report the monitoring of polymer-assisted grinding reactions using <i>in situ</i> X-ray powder diffraction, revealing that reaction rate is almost double compared to neat grinding and independent of the molecular weight and amount of used polymer additives.<br>

In situ Time-Resolved Small-Angle X-ray Scattering Reveals the Formation Kinetics of Polyelectrolyte Complex Micelles

2019 ◽  
Author(s):  
Hao Wu ◽  
Jeffrey Ting ◽  
Siqi Meng ◽  
Matthew Tirrell
Keyword(s):  
Small Angle ◽  
Self Assembly ◽  
Electrostatic Interactions ◽  
Polyelectrolyte Complex ◽  
Time Resolved ◽  
X Ray ◽  
X Ray Scattering ◽  
Kinetics Of ◽  
Ray Scattering

We have directly observed the <i>in situ</i> self-assembly kinetics of polyelectrolyte complex (PEC) micelles by synchrotron time-resolved small-angle X-ray scattering, equipped with a stopped-flow device that provides millisecond temporal resolution. This work has elucidated one general kinetic pathway for the process of PEC micelle formation, which provides useful physical insights for increasing our fundamental understanding of complexation and self-assembly dynamics driven by electrostatic interactions that occur on ultrafast timescales.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

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