Deep UV excited muscle cell autofluorescence varies with the fibre type

The Analyst ◽  
2015 ◽  
Vol 140 (12) ◽  
pp. 4189-4196 ◽  
Author(s):  
Caroline Chagnot ◽  
Annie Vénien ◽  
Frédéric Peyrin ◽  
Frédéric Jamme ◽  
Matthieu Réfrégiers ◽  
...  
Keyword(s):  
Muscle Cell ◽  
Fibre Type ◽  
Label Free ◽  
Deep Uv ◽  
Fibre Typing

DUV autofluorescence microspectroscopy allows label free fibre typing in muscles.

Label-free multi-spectral deep UV imaging of prostate cancer tissue

2021 ◽  
Author(s):  
Soheil Soltani ◽  
Ashkan Ojaghi ◽  
Adeboye O. Osunkoya ◽  
Francisco E. Robles
Keyword(s):  
Prostate Cancer ◽  
Cancer Tissue ◽  
Label Free ◽  
Uv Imaging ◽  
Prostate Cancer Tissue ◽  
Deep Uv

scholarly journals Label-Free Bacterial Imaging with Deep-UV-Laser-Induced Native Fluorescence

2010 ◽  
Vol 76 (21) ◽  
pp. 7231-7237 ◽  
Author(s):  
Rohit Bhartia ◽  
Everett C. Salas ◽  
William F. Hug ◽  
Ray D. Reid ◽  
Arthur L. Lane ◽  
...  
Keyword(s):  
Fluorescent Dyes ◽  
Spatial Scales ◽  
Single Cells ◽  
Deep Ocean ◽  
Imaging Method ◽  
Label Free ◽  
Deep Biosphere ◽  
Native Fluorescence ◽  
Deep Uv

ABSTRACT We introduce a near-real-time optical imaging method that works via the detection of the intrinsic fluorescence of life forms upon excitation by deep-UV (DUV) illumination. A DUV (<250-nm) source enables the detection of microbes in their native state on natural materials, avoiding background autofluorescence and without the need for fluorescent dyes or tags. We demonstrate that DUV-laser-induced native fluorescence can detect bacteria on opaque surfaces at spatial scales ranging from tens of centimeters to micrometers and from communities to single cells. Given exposure times of 100 μs and low excitation intensities, this technique enables rapid imaging of bacterial communities and cells without irreversible sample alteration or destruction. We also demonstrate the first noninvasive detection of bacteria on in situ-incubated environmental experimental samples from the deep ocean (Lo'ihi Seamount), showing the use of DUV native fluorescence for in situ detection in the deep biosphere and other nutrient-limited environments.

scholarly journals Label-free imaging and classification of live P. falciparum enables high performance parasitemia quantification without fixation or staining

2021 ◽  
Vol 17 (8) ◽  
pp. e1009257
Author(s):  
Paul Lebel ◽  
Rebekah Dial ◽  
Venkata N. P. Vemuri ◽  
Valentina Garcia ◽  
Joseph DeRisi ◽  
...  
Keyword(s):  
Limit Of Detection ◽  
Uv Light ◽  
Life Stage ◽  
Superior Performance ◽  
Ring Stage ◽  
Label Free ◽  
Uv Illumination ◽  
Near Ultraviolet ◽  
Deep Uv

Manual microscopic inspection of fixed and stained blood smears has remained the gold standard for Plasmodium parasitemia analysis for over a century. Unfortunately, smear preparation consumes time and reagents, while manual microscopy is skill-dependent and labor-intensive. Here, we demonstrate that deep learning enables both life stage classification and accurate parasitemia quantification of ordinary brightfield microscopy images of live, unstained red blood cells. We tested our method using both a standard light microscope equipped with visible and near-ultraviolet (UV) illumination, and a custom-built microscope employing deep-UV illumination. While using deep-UV light achieved an overall four-category classification of Plasmodium falciparum blood stages of greater than 99% and a recall of 89.8% for ring-stage parasites, imaging with near-UV light on a standard microscope resulted in 96.8% overall accuracy and over 90% recall for ring-stage parasites. Both imaging systems were tested extrinsically by parasitemia titration, revealing superior performance over manually-scored Giemsa-stained smears, and a limit of detection below 0.1%. Our results establish that label-free parasitemia analysis of live cells is possible in a biomedical laboratory setting without the need for complex optical instrumentation. We anticipate future extensions of this work could enable label-free clinical diagnostic measurements, one day eliminating the need for conventional blood smear analysis.

Label-free analysis in chip electrophoresis applying deep UV fluorescence lifetime detection

2011 ◽  
Vol 32 (22) ◽  
pp. 3108-3114 ◽  
Author(s):  
Reinhild Beyreiss ◽  
Stefan Ohla ◽  
Stefan Nagl ◽  
Detlev Belder
Keyword(s):  
Fluorescence Lifetime ◽  
Label Free ◽  
Uv Fluorescence ◽  
Deep Uv ◽  
Fluorescence Lifetime Detection

scholarly journals Two-photon fluorescence lifetime for label-free microfluidic droplet sorting

2021 ◽  
Author(s):  
Sadat Hasan ◽  
Maximilian E. Blaha ◽  
Sebastian K. Piendl ◽  
Anish Das ◽  
David Geissler ◽  
...  
Keyword(s):  
Fluorescence Lifetime ◽  
Single Photon ◽  
Photon Counting ◽  
Droplet Microfluidics ◽  
Label Free ◽  
Single Photon Counting ◽  
Two Photon ◽  
Deep Uv ◽  
Droplet Sorting ◽  
532 Nm

AbstractMicrofluidic droplet sorting systems facilitate automated selective micromanipulation of compartmentalized micro- and nano-entities in a fluidic stream. Current state-of-the-art droplet sorting systems mainly rely on fluorescence detection in the visible range with the drawback that pre-labeling steps are required. This limits the application range significantly, and there is a high demand for alternative, label-free methods. Therefore, we introduce time-resolved two-photon excitation (TPE) fluorescence detection with excitation at 532 nm as a detection technique in droplet microfluidics. This enables label-free in-droplet detection of small aromatic compounds that only absorb in a deep-UV spectral region. Applying time-correlated single-photon counting, compounds with similar emission spectra can be distinguished due to their fluorescence lifetimes. This information is then used to trigger downstream dielectrophoretic droplet sorting. In this proof-of-concept study, we developed a polydimethylsiloxane-fused silica (FS) hybrid chip that simultaneously provides a very high optical transparency in the deep-UV range and suitable surface properties for droplet microfluidics. The herein developed system incorporating a 532-nm picosecond laser, time-correlated single-photon counting (TCSPC), and a chip-integrated dielectrophoretic pulsed actuator was exemplarily applied to sort droplets containing serotonin or propranolol. Furthermore, yeast cells were screened using the presented platform to show its applicability to study cells based on their protein autofluorescence via TPE fluorescence lifetime at 532 nm. Graphical abstract

scholarly journals Prostate cancer histopathology with label-free multispectral deep UV microscopy quantifies phenotypes of tumor grade and aggressiveness

2021 ◽  
Author(s):  
Soheil Soltani ◽  
Ashkan Ojaghi ◽  
Hui Qiao ◽  
Nischita Kaza ◽  
Xinyang Li ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Patients ◽  
Low Cost ◽  
Tumor Grade ◽  
Histopathological Analysis ◽  
Basal Cells ◽  
Label Free ◽  
Generative Adversarial Network ◽  
Tissue Sections ◽  
Deep Uv

Abstract Identifying prostate cancer patients that are harboring aggressive forms of prostate cancer remains a significant clinical challenge. To help address this problem, we develop an approach based on multispectral deep-ultraviolet (UV) microscopy that provides novel quantitative insight into the aggressiveness and grade of this disease. First, we find that UV spectral signatures from endogenous molecules give rise to a phenotypical continuum that differentiates critical structures of thin tissue sections with subcellular spatial resolution, including nuclei, cytoplasm, stroma, basal cells, nerves, and inflammation. Further, we show that this phenotypical continuum can be applied as a surrogate biomarker of prostate cancer malignancy, where patients with the most aggressive tumors show a ubiquitous glandular phenotypical shift. Lastly, we adapt a two-part Cycle-consistent Generative Adversarial Network to translate the label-free deep-UV images into virtual hematoxylin and eosin (H&E) stained images. Agreement between the virtual H&E images and the gold standard H&E-stained tissue sections is evaluated by a panel of pathologists who find that the two modalities are in excellent agreement. This work has significant implications towards improving our ability to objectively quantify prostate cancer grade and aggressiveness, thus improving the management and clinical outcomes of prostate cancer patients. This same approach can also be applied broadly in other tumor types to achieve low-cost, stain-free, quantitative histopathological analysis.

scholarly journals Prostate Cancer Histopathology Using Label-free Multispectral Deep-UV Microscopy Quantifies Phenotypes of Tumor Aggressiveness and Enables Multiple Diagnostic Virtual Stains

2021 ◽  
Author(s):  
Soheil Soltani ◽  
Ashkan Ojaghi ◽  
Hui Qiao ◽  
Nischita Kaza ◽  
Xinyang Li ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Patients ◽  
Low Cost ◽  
Histopathological Analysis ◽  
Label Free ◽  
Generative Adversarial Network ◽  
Tissue Sections ◽  
Adversarial Network ◽  
Important Challenge ◽  
Deep Uv

Abstract Identifying prostate cancer patients that are harboring aggressive forms of prostate cancer remains a significant clinical challenge. Here we develop an approach based on multispectral deep-ultraviolet (UV) microscopy that provides novel quantitative insight into the aggressiveness and grade of this disease, thus providing a new tool to help address this important challenge. We find that UV spectral signatures from endogenous molecules give rise to a phenotypical continuum that provides unique structural insight (i.e., molecular maps or “optical stains") of thin tissue sections with subcellular (nanoscale) resolution. We show that this phenotypical continuum can also be applied as a surrogate biomarker of prostate cancer malignancy, where patients with the most aggressive tumors show a ubiquitous glandular phenotypical shift. In addition to providing several novel “optical stains” with contrast for disease, we also adapt a two-part Cycle-consistent Generative Adversarial Network to translate the label-free deep-UV images into virtual hematoxylin and eosin (H&E) stained images, thus providing multiple stains (including the gold-standard H&E) from the same unlabeled specimen. Agreement between the virtual H&E images and the H&E-stained tissue sections is evaluated by a panel of pathologists who find that the two modalities are in excellent agreement. This work has significant implications towards improving our ability to objectively quantify prostate cancer grade and aggressiveness, thus improving the management and clinical outcomes of prostate cancer patients. This same approach can also be applied broadly in other tumor types to achieve low-cost, stain-free, quantitative histopathological analysis.

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