Här kommer svaret på helgens pysselutmaning.En nöt riktigt svår att knäcka som det verkar.
Dock fick jag in det rätta svaret tidigt ska erkännas.Bara 2 tim efter jag la ut utmaningen levererade phis,nye supergrävare det rätta svaret.Den tidigare contendern Oscar promoveras nu till officiell supergrävare och honoreras därmed med en guldstjärna.
Som den anspråkslöse man vår nye supergrävare är bad han mig att inte lägga ut det rätta svaret så tidigt.
Det skulle ju förstöra för andra ev grävare som var i färd med att sätta spaden i myllan.Och det höll jag med om.Men nu är det dax att ge det rätta svaret.
2 tekniska forskare,Devin Lange och Alexander Lex, från visualization design lab på University of Utah har under 18 månader arbetat med att ta fram ett verktyg som klarar att bearbeta en större mängd data.
Jag kallar det Big Data.
Verktyget/tekniken är benämnd Loon (visual analysis tool) och utgår från insamlade cancercellsobservationer.
Lange och Lex har under 18 månader samarbetat med 2 forskningsgrupper (collaborators) på universitetet,bägge med inriktning på cancerforskning.Zangle Lab och JudsonTorres Lab.Från dessa labb har L+L fått tillgång till tiotusentals cellobservationer.Labben skiljer sig åt ur 2 aspekter,inriktning och instrumentering.
Zangle Lab forskar kring immunonkologi och använder sig av företaget Cellinks instrument Livecyte.
JudsonTorres Lab forskar kring hudcancer och använder sig av PHI`s eminenta HoloMonitor vilket de flesta phi,are säkert vet.
Bägge labben har varit med att utveckla Loon.
Vad går då detta verktyg ut på,vad är dess användningsområden?
Jo,det är alltså en mjukvara som exempelvis är användbar för att identifiera vilket läkemedel som är mest effektivt att sätta in på olika sorters cancer.
L+L har i nedanstående studie undersökt 2 cancertyper,bröstcancer och hudcancer.
De har laborerat med olika kända cancerläkemedel och fått fram vilket som är mest effektivt på resp cancer.
Vi talar alltså om skräddarsydd cancerbehandling.Det då varje individs cancer är unik i sin uppbyggnad och därför svarar olika på olika sorters läkemedel.Med Loon kommer läkare att kunna ta fram exakt vilket läkemedel som bäst "biter" på drabbads cancer.Min tolkning är att detta förutsätter tillgång till antingen LiveCyte eller HoloMonitor för behandlaren.
Men till studien.
(pdf)
Devin Lange, Eddie Polanco, Robert Judson-Torres, Thomas Zangle, Alexander Lex
Created: May 04, 2021
| Last edited: May 05, 2021
Abstract
Which drug is most promising for a cancer patient? This is a question a new microscopy-based approach for measuring the mass of individual cancer cells treated with different drugs promises to answer in only a few hours. However, the analysis pipeline for extracting data from these images is still far from complete automation: human intervention is necessary for quality control for preprocessing steps such as segmentation, to adjust filters, and remove noise, and for the analysis of the result. To address this workflow, we developed Loon, a visualization tool for analyzing drug screening data based on quantitative phase microscopy imaging. Loon visualizes both, derived data such as growth rates, and imaging data. Since the images are collected automatically at a large scale, manual inspection of images and segmentations is infeasible. However, reviewing representative samples of cells is essential, both for quality control and for data analysis. We introduce a new approach of choosing and visualizing representative exemplar cells that retain a close connection to the low-level data. By tightly integrating the derived data visualization capabilities with the novel exemplar visualization and providing selection and filtering capabilities, Loon is well suited for making decisions about which drugs are suitable for a specific patient.
Utvalda urklipp. (understrykningar är mina egna)
1 INTRODUCTION
Automatically acquired, large-scale microscopy data is an increasingly
important tool in life-science research and medical practice. Areas such
as brain connectomics create high-resolution images using electron microscopes of neurons and use segmentation to reconstruct the connectivity of the brain. Similarly, high-throughput screening to
observe the effect of drug candidates on their cell lines is a frequently
used method in pharmacological research. These approaches have
in common that the bottleneck has moved from acquiring image data
to processing and analyzing the data.
With the many thousands if not
millions of images captured, these pipelines heavily rely on automatic
image analysis processes, such as segmentation, as the basis for deriving datasets of interest.
However, completely automated setups have
proven elusive, not least due to the heterogeneity of the images and
biological structures captured. Analysts face many challenges,
including having to conduct quality control of automatic processes
(checking segmentation and tracking) and adapting the analysis for the particular experiment.
To address these challenges, analysts need
support through interactive visual analysis systems.
In this paper, we introduce Loon, a visual analysis tool for a novel
type of application: screening specific patient’s tumor samples for a
variety of cancer drugs using quantitative phase imaging (QPI) data.
Our collaborators are developing this novel technology and the associated data analysis pipeline.
The goal is to rapidly (i.e., within 1–2
days) experimentally determine which known cancer drug inhibits cell
growth, thereby taking a leap towards personalized medicine.
The
heterogeneous nature of the data — for different tumor types taken
from different patients — requires a flexible visual analysis solution.
Our contribution is two-fold: on the one hand, we contribute a design
study, based on a detailed analysis of the domain problem, which results
in a functioning and deployed software tool.
On the other hand, we
also contribute a novel technique to visualize representative exemplars
of cells by sampling cells along user-specified data dimensions.
This
approach enables analysts to both quickly validate pre-processing steps
such as segmentation and tracking, and analyze the properties of cells in
different conditions, thereby making visual analysis of microscopy data
feasible even for large datasets.
We argue that this technique can be
applied broadly to other imaging/segmentation problems and beyond.
We validate our design in two case studies ––– one focused on quality
control, the other applied to data analysis — and through examples
with two different datasets.
2.1 Cell Microscopy Data Visualization
Automated microscopy approaches, and in particular live-cell imaging
— where cells are observed while they develop — have entered the
mainstream of biological research in the last decade.
Data analysis and
visualization of imaging data have been identified as critical in the life
science community, and a recent survey by Pretorius et al.
has recognized it as an important emergent visualization research area.
Our collaborators use both custom and commercial systems. The
commercial systems they use — Livecyte and HoloMonitor
— are packaged with analytical software. Both tools provide various
statistical plots to aid in the analysis. Although the quality of the images
can be easily monitored with these software packages, determining the
quality of the segmentation and tracking and filtering inappropriately
segmented objects is onerous. Our approach of providing cell track
exemplars that recalculate in real-time as filters are applied to object
features makes quality checks much more efficient, yet our tool remains
compatible with the data generated from these commercial platforms.
3 BIOLOGICAL BACKGROUND
Cancer is a complex and dynamic disease with individual tumors presenting substantial genomic and transcriptomic heterogeneity. This
makes it difficult to select the appropriate therapy for treating an individual cancer patient. Functional precision medicine seeks to guide
treatment decisions using assays that measure the response of patientderived tumor cells to candidate therapies. Our collaborators
are demonstrating the use of quantitative phase imaging as a method
to measure the growth rate of individual cancer cells in response to
chemotherapy. Effective chemotherapies reduce cell growth rates at
low concentrations, indicating sensitivity. This work is therefore a step
towards a functional precision assay.
QPI measures the phase shift of light as it passes through and interacts with cell mass. This phase shift is proportional to cell mass.
QPI has previously been applied to rapidly (within 5–10 h) measure
chemotherapy sensitivity and changes in cell phenotype associated
with metastatic dissemination with single-cell resolution. QPI
is, therefore, an ideal method for assessing the response of cancer cells
to potential therapies.
4 DATASET DESCRIPTION
The Zangle Lab and the Judson-Torres Lab are currently using Loon
to analyze the datasets they collect.
In this paper, we focus on one
dataset from each lab as examples, though Loon is compatible with
many similar datasets.
The second example dataset we describe in a case study is provided
by the Judson-Torres Lab and is designed to investigate metastatic
melanoma — skin cancer that has spread to other parts of the body.
A tumor can contain subpopulations of cells that are either resistant
or sensitive to treatment from a particular drug. In this experiment,
the Judson-Torres Lab models this heterogeneity by combining two
human melanoma cell lines.
One that is resistant to drugs, and one that
is sensitive. With this, they can carefully control the ratios of the two cell types. In their experiment, they used a multi-well plate to separate
four groups of cell mixture ratios (100%, 80%, 20%, 0% resistant
cells). For each group, they exposed it to the drug vemurafenib at five
different concentrations, as well as a control group with no drug.
These
conditions were imaged at 28 locations every 30 minutes for 48 hours,
resulting in a total of 2,716 images (at resolution 768x768), 439,699
segmented cells, and 111,151 cell tracks.
In general, these datasets contain thousands or tens of thousands of
images (with resolutions between 400x300 to 1920x1200) which are
assigned to conditions. Conditions are typically a combination of a
type of drug and its concentration, or controls. Each image contains
dozens or hundreds of cells.
In a preprocessing step, our collaborators
compute segmentation labels and derived numerical attributes, such as
the area of a cell, the estimated mass of a cell, its position, attributes
describing morphology (“shape factors”), etc. The position and mass
of the cells are then used to track cells at the same location over time
— resulting in tracks — for which the change in attributes over time
can be measured and visualized. We supplement this data with derived
data computed on the fly on both the cell (normalized mass) and tracks
level (a growth rate).
8.2 Refining The Tracking for a Melanoma Dataset
In recent years, great strides have been made in the treatment of
metastatic melanoma — the deadliest of skin cancer. These include targeted therapies against common oncogenic mutations and immunotherapies.
Both therapies can be effective, but neither works with 100%
efficacy. This is due to the heterogeneous nature of these types of
cancer cell populations. Determining which therapy will ultimately
be most successful in preventing metastasis requires a better understanding of the effect drugs have on these different subpopulations of
cells.
A functional precision medicine approach that predicts response
to targeted therapies could aid oncologists in this difficult and critical
decision.
To answer these questions, Dr. Judson-Torres’ lab is running
QPI experiments on their commercial platforms.
Dr. Judson Torres similarly commented:
The workflow we find most useful in Loon is the ability
to quickly visualize the mass, tracking information, and
exemplar images of object groups based upon feature filters. The dry mass, track length, and, of course, the actual
image of the segmented objects are all critical for conducting quality control — identifying which objects represent
cells or colonies of interest and which are artifact from the
segmentation. This process has previously been extremely
tedious with our existing approaches, so much so that we
had started to just accept a certain amount of noise in our
data due to segmentation artifacts. In addition, one of our
main uses of QPI approaches is to identify groups of phenotypically distinct cells within heterogenous populations.
Our previous approaches first required analyses of featurelevel data to identify distinct groups of objects followed
by cross-referencing representatives of each group in the
images to determine whether the observed feature clusters
were real or artifact — a time intensive process. The ability to select groups based upon features and, in real time,
visualize exemplars from each group enables us to rapidly
identify and verify distinct groups of cells.
Min kommentar
Mjukvaran Loon (ett enklare set kan laddas ner här) kan alltså på 1-2 dagar bedöma vilket läkemedel som bäst har ihjäl dessa förb...ade cancerceller för varje individuellt drabbad.Vi har spekulerat kring skräddarsydd cancerbehandling ett antal år. PHI-folk som Zahra El-Schich spådde 2018 följande :
- In the near future, you could have a HoloMonitor in every clinic.
Imagine using time-lapse recordings on tumor samples from patients.
You could also customize cancer treatment for every patient by just using a simple tumor test.
Then you could test different cancer treatments on the tumor sample, which concentration to use for instance, and see which treatment fits which patient.
All patients are different. One patient might only need the lowest dose of chemo therapy, and another patient will need to have the highest dose, and for some patients the highest dose isn’t enough.
Instead of testing on the patients live, treatments could be tested on the tumor sample.
This would give every patient better cancer treatment and a better chance of being declared healthy, and also reduce risks like resistance to treatment.
We are all genetically different, and cancer heritage from different factors.
Breast cancer for instance can be caused by hormones or genetical factors; therefore two people with the same diagnosis, like breast cancer, need different treatments.
A sample can show which protein the cancer cells are carrying and help to choose treatment.
Since we are different – why should we have the same cancer treatment?
Imagine using time-lapse recordings on tumor samples from patients.
You could also customize cancer treatment for every patient by just using a simple tumor test.
Then you could test different cancer treatments on the tumor sample, which concentration to use for instance, and see which treatment fits which patient.
All patients are different. One patient might only need the lowest dose of chemo therapy, and another patient will need to have the highest dose, and for some patients the highest dose isn’t enough.
Instead of testing on the patients live, treatments could be tested on the tumor sample.
This would give every patient better cancer treatment and a better chance of being declared healthy, and also reduce risks like resistance to treatment.
We are all genetically different, and cancer heritage from different factors.
Breast cancer for instance can be caused by hormones or genetical factors; therefore two people with the same diagnosis, like breast cancer, need different treatments.
A sample can show which protein the cancer cells are carrying and help to choose treatment.
Since we are different – why should we have the same cancer treatment?
Nu är vi alltså nästan där.Loon som "vår" forskare Robbie Torres varit med om att utveckla kommer behöva genomgå flera tester innan det kan launchas.Men att detta verktyg redan visar upp så bra resultat är mycket hoppingivande.Framförallt för världens alla cancerdrabbade.Man behöver inte vara livrädd för att cancern hinner före innan läkarna hittat det mest effektiva läkemedlet.
Ett mycket stort + denna studie visar är att bjässen Cellink kommer vara ytterst motiverade att få Loon accepterat och använt,det då deras eget instrument Livecyte har så mycket att vinna på dess användande.
I kölvattnet på Cellinks ansträngningar (stålar) att få Loon accepterat kan sen PHI som 2:a dra nytta av det utan några större kostnader. Jag tycker denna studie med dess resultat är enastående bra nyheter.
VD Egelberg får bjussa Robbie Torres på en rejäl barrunda nästa gång han besöker USA. 😎
Extrabonus. På denna länk kan ni se Loon in action.
Mvh the99
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