För några timmar sen släppte PHI presentationen Kersti Alm gjorde till statliga amerikanska standardiseringsinstitutet NIST 1/12. Detta event alltså.
238 prenumeranter
10 visningar 9 dec. 2022
Our CSO Kersti Alm presents a lightning talk at the 2022 #NIST (The National Institute of Standards and Technology) workshop on "Measurement Needs for #Biofabrication of Tissue Engineered Medical Products"!
She introduces the label-free #HoloMonitor technology of #digitalholographicmicroscopy and its unique advantages for minimally destructive #cellphenotype measurements.
More information on the event website:
https://www.nist.gov/news-events/even...Gå in på länken och kolla presentationen.
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Ny forskningsrapport från idag 9/12.Grävare Oscar var snabb att mejla över den så all cred till honom.
Dagens forskningsrapport är intressant ur flera synvinklar. 6 forskare från 5 olika säten, m länder som Sverige,Kina och Tjeckien. Studien har förmodligen letts av "vår" professor Anette Gjörloff-Wingren då hon är en av deltagarna. Två andra forskare är kända erfarna HoloMonitoranvändare, Zahra El-Schich och Jenny L. Persson. Ämnet är cancer (såklart), närmare bestämt tjock och ändtarmscancer.
" Tjock- och ändtarmscancer (kolorektal cancer) är den tredje
vanligaste cancerformen i Sverige.
Varje år drabbas över 4 700 personer
av tjocktarmscancer och omkring 2 100 av ändtarmscancer. Medelåldern på
dem som insjuknar i cancerformerna ligger på 73 respektive 70 år.
Endast
fem procent är under 50 år.
Tjocktarmscancer är lika vanligt hos män som hos kvinnor medan ändtarmscancer är vanligare hos män.
Femårsöverlevnaden har förbättrats för både tjocktarms- och ändtarmscancer under de senaste decennierna.
Den
relativa överlevnaden (i jämförelse med befolkningen i Sverige) för
tjocktarmscancer är ca 68 procent, fem år efter diagnos. Motsvarande
siffra för ändtarmscancer är 69 procent."
I artikeln hänvisar forskarna till en tidigare studie där forskarna på egen hand,som jag förstår det, byggt ihop ett enklare fluo och holo mikroskop. Det för att kunna studera vita och röda blodkroppar simultant tillsammans med cancerceller. Resultatet är ganska häpnadsväckande.
- Nissim et al. recently showed in their system that blood cells mixed with colorectal cancer cells could be automatically discriminated on the level of individual cell types, by using label-free holographic flow cytometry.
By their custom-built optical system operated under flow, they received single-cell holograms in real time and applied image processing and machine learning. The database created found features that could differ between cancer cells and various blood cells.
Studien man hänvisar till är : Real-Time Stain-Free Classification of Cancer Cells and Blood Cells Using Interferometric Phase Microscopy and Machine Learning (Nissim et al.)
Fluo tillsammans med Holotekniken kunde särskilja cancerceller från blodkroppar.Nu är undertecknad enbart en simpel lekman utan erfarenhet av labbande, men min tolkning är att särskiljandet får betecknas som häpnadsväckande. Tänk här PHI`s kombo som snart är ute på marknaden. Några bekräftande studier med den som visar på samma resultat, då ni!
Men till aktuell studie.DHC,Digital Holographic Cytometry är alltså HoloMonitor de refererar till i studien.
Håll ögonen på alla passager där man nämner DHC.
Published: 9 December 2022
Abstract
:
Colorectal cancer (CRC) is the second most
metastatic disease with the majority of cases detected in Western
countries. Metastases are formed by circulating altered phenotype tumor
cells causing 20% of CRC related deaths. Metastatic cells may show
higher expression of surface molecules such as CD44, and changes in
morphological properties are associated with increased invasiveness and
poor prognosis. In this study, we intended to mimic the environment for
metastasizing cells. Here, we used digital holographic cytometry (DHC)
analysis to determine cellular morphological properties of three
metastatic and two non-metastatic colorectal cancer cell lines to show
differences in morphology between the CRC cells and peripheral blood
mononuclear cells (PBMCs). By establishing differences in cell area,
cell thickness, cell volume, and cell irregularity even when the CRC
cells were in minority (5% out of PBMCs), DHC does discriminate between
CRC cells and the PBMCs in vitro. We also analyzed the epithelial marker
EpCAM and migration marker CD44 using flow cytometry and demonstrate
that the CRC cell lines and PBMC cells differ in EpCAM and CD44
expression.
Here, we present DHC as a new powerful tool in
discriminating cells of different sizes in suspension together with a
combination of biomarkers.
Keywords:
cell morphology; CD44; circulating tumor cell; colorectal cancer; digital holographic cytometry; EpCAM
1. Introduction
Colorectal
cancer (CRC), a highly spread tumor disease, is the third and second
most common cancer in men and women, respectively.
Owing to changes in diet preferences and increasing living standards in
Western countries, CRC has become a major cancer disease with more than
1.800.000 of the new cases worldwide.
Despite a monitoring program consisting of stool-based tests and
colonoscopy, and intensive chemo- or immunotherapeutic interventions,
20% of patients diagnosed with CRC initially present with metastatic
disease and subsequently 35% of patients with stage III eventually
relapse and later die from CRC.
Circulating
tumor cells (CTCs) are detached from the original tumor and spread
through the bloodstream to other organs, forming distant lesions to
establish metastases.
In this process, the epithelial-to-mesenchymal transition (EMT) plays a
crucial role since it is characterized by losing intercellular adhesion
and epithelial polarization.
The acquisition of the mesenchymal phenotype is accompanied by
increased motility and invasiveness. Besides different cellular
morphology, cells may during EMT alter the expression of signaling and
surface molecules potentially affecting the tumor microenvironment or
contribute to the avoidance of immune surveillance.
Digital
holographic cytometry (DHC) is a novel imaging technique for
observations of cell samples in their native monolayer settings.
DHC functions on the principle of quantitative phase imaging (QPI)
providing high spatial resolution and quantification of cellular
morphological parameters such as volume, area, and optical thickness
from recorded holograms.
Since it does not require any fluorescent labels or staining, DHC
allows for long-term time-lapse experiments with precise observations
and with no phototoxic effects on the cells compared to differential
interface contrast microscopy.
Recently, DHC has been used for various biological applications such as evaluation of cell proliferation, detection and classification of cell death, nanoparticle uptake, and motility and migration studies.
Moreover, we recently used DHC to discriminate between breast cancer
cells and leukemic cells according to their morphological parameters.
Leukemic cells were significantly smaller in the investigated parameters
optical thickness, volume, and area compared to epithelial breast
cancer cells.
This finding paved the way for using DHC as a tool for the in vitro
circulating tumor cell-model, now further explored in this study by
combining morphology measurements with epithelial and mesenchymal
surface markers.
Although CTCs have different
characteristics, the detection of CTCs is highly complicated since the
occurrence in the bloodstream is very sparse compared to human blood
cells. Up to date, flow cytometry, Raman spectroscopy, microfluidic devices, immunoassays or methods based on the polymerase chain reaction (PCR)
were utilized to discriminate the CTCs from other cells in blood or in
liquid biopsies.
Indeed, optical tomography provide a more complete
mapping of the biological sample in combination with QPI. The 3D refractive index will allow studies of subcellular and micrometer structures of the cells.
By combining all time-lapse quantitative phase maps, each orientation of the cell can be obtained.
For high-throughput 3D cell measurements, microfluidic devices have
been integrated to tomographic phase microscopy including work on red
blood cells and CTCs.
Nissim et al. recently showed in their system that blood cells mixed
with colorectal cancer cells could be automatically discriminated on the
level of individual cell types, by using label-free holographic flow
cytometry.
By their custom-built optical system operated under flow, they received
single-cell holograms in real time and applied image processing and
machine learning. The database created found features that could differ
between cancer cells and various blood cells. Moreover, cell sorting was
included in a tomographic interferometry approach by rotating cells in a
flow with dielectrophoretic forces.
Interestingly, here the authors used three types of CRCs, HT29, SW-480 and SW-620 together with blood cells.
The CRC cell type mixed with blood cells, i.e., the liquid biopsy, were
first enriched by filtration and then the CRCs cells were classified
during flow using machine learning, and then isolated by using
activating DEP.
Most elegantly, the authors also built in a fluorescence
imaging system for an external validation where only the cancer cells
emitted fluorescence light. Moreover, a recent study with an approach
aiming for clinical use, also showed classification of cancer cells
based on the cell spatial and temporal fluctuations . The classification method can both be used to detect CTCs from blood and analyse cancer cells from tissue or solid tumors.
Despite
the recent progress, only the CellSearch protocol is the currently
approved method by the US Food and Drug Administration (FDA) for breast,
prostate, and CRC.
It works on the principle of positive cell enrichment and detection of
cell adhesion molecules, but the CellSearch protocol has shown low
specificity for the reoccurrence of the CTCs after adjuvant cancer
therapy.
It has been reported that several drugs, e.g., bevacizumab,
alter expression of epithelial cell adhesion molecule (EpCAM).
Therefore, there is a demand to identify CTCs using novel parameters
allowing their precise detection independently on the cancer treatment
or stage.
In the present study, we have
performed DHC-based morphological analyses on a set of colon cancer cell
lines and peripheral blood mononuclear cells (PBMCs).
Besides, we used
DHC to distinguish colon cancer cells from white blood cells (WBC) in
suspension in vitro, to mimic an environment of the CTCs where they are
surrounded by blood cells.
In conclusion, we show that DHC serves as a
useful technique for discriminating CRC cells from WBC in vitro.
Subsequently, we analyzed the expression of the epithelial marker EpCAM
and the mesenchymal marker CD44 to better characterize the cell
populations.
Materials and Methods (urval)
2.4. DHC and Image Analysis
Cells were washed with 5 mL of PBS and harvested by trypsinization, washed twice in 2 mL of PBS and 1 × 106 cells were resuspended in 100 µL of PBS. 10 µL of the suspension was added to CountessTM
cell chamber slide (ThermoFisher Scientific).
Morphological parameters
of cells in suspension were then obtained using HoloMonitor M4 (Phase
Holographic Imaging AB, PHIAB, Lund, Sweden) and HoloMonitor proprietary
software AppSuite (PHIAB). Four separate experiments with 30 images in
each were collected during ten minutes, and ten representative images
from each experiment were chosen and analyzed. Image capture was
performed with a low-intensity 635 nm diode laser, which prevents
phototoxicity. The optical magnification used was 10× and the resolution
was 0.5 μm. The cells in the images are segmented to extract
information on each imaged cell separately, and thereby cellular
parameters such as area, optical thickness and optical volume were
obtained. For cell mixes, 1 × 106
cells were resuspended and mixed in 100 µL of PBS with ratios of 95 +
5% or 90 + 10% of PBMCs to COLO 205 cells. 10 µL of the mixed suspension
was added to the CountessTM cell chamber slide and analyzed
using HoloMonitor and AppSuite.
Two separate experiments with 30 images
in each were collected and ten representative images from one experiment
were chosen and analyzed."
I slutet av artikeln skriver de b.l.a :
- We have previously analyzed a collection of breast cancer cell lines in
an ”in vitro circulating tumor cell model” for analysis of sialic acid
as a tumor marker and size parameters using DHC, thus distinguishing
larger epithelial breast cancer cells from small blood cells. The breast
cancer cell lines were significantly larger compared to white blood
cell lines, Jurkat and THP-1 cells.
- In this study, we classified the cellular morphology of a set of CRC
cell lines. Next, we compared them regarding the expression of the EMT
markers EpCAM and CD44.
We demonstrated based on their morphological
properties, DHC is a promising tool for discrimination between CRC cells
and PBMCs. By this, we believe that the evaluation of cellular
morphology and surface markers would add another piece to the CTC
detection puzzle.
Min kommentar
Forskarna är onekligen helsålda på DHC-tekniken och ser framtida behov av att använda den till cancerstudier inom alltfler områden. Precis samma åsikt som undertecknad har skrivit om tidigare.
Kul att även forskarna håller med mig. 😎
Nu ser vi fram mot släppet av fluo/holo-kombin. De första studierna med kombon kommer bli spännande att ta del av.
Mvh the99
