Hybrid microscope could make digital biopsies more accessible to labs, clinics

Date

02/13/20
Machine-learning tools can analyze the data from the infrared-optical hybrid microscope to create digital versions of standard dyes, left, or to identify tissue types based on their chemical composition, right. Image courtesy of Rohit Bhargava

By adding infrared capability to the standard optical microscope, researchers at the University of Illinois at Urbana-Champaign hope to bring cancer diagnosis into the digital era.

Pairing infrared measurements with high-resolution optical images and machine learning algorithms, the researchers created digital biopsies that closely correlated with traditional pathology techniques and outperformed state-of-the-art infrared microscopes.  

Led by Rohit Bhargava — an affiliate faculty member in the Department of Chemistry, a professor of bioengineering and the director of the Cancer Center at Illinois — the group published its results in the Proceedings of the National Academy of Sciences.

“The advantage is that no stains are required, and both the organization of cells and their chemistry can be measured. Measuring the chemistry of tumor cells and their microenvironment can lead to better cancer diagnoses and better understanding of the disease,” Bhargava said.

The gold standard of tissue pathology is to add dyes or stains so that pathologists can see the shapes and patterns of the cells under a microscope. However, it can be difficult to distinguish cancer from healthy tissue or to pinpoint the boundaries of a tumor, and in many cases diagnosis is subjective.

Head shot of Professor Rohit Bhargava
Photo by L. Brian Stauffer

“For more than a century, we have relied on adding dyes to human tissue biopsies to diagnose tumors. However, the shape and color induced by the dye provide very limited information about the underlying molecular changes that drive cancer,” Bhargava said.

Technologies like infrared microscopy can measure the molecular composition of tissue, providing quantitative measures that can distinguish cell types. Unfortunately, infrared microscopes are expensive, and the samples require special preparation and handling, making them impractical for many clinical and research settings. 

Bhargava’s group developed its hybrid microscope by adding an infrared laser and a specialized microscope lens, called an interference objective, to an optical camera. The infrared-optical hybrid measures both infrared data and a high-resolution optical image with a light microscope – the kind ubiquitous in clinics and labs.

Image of a digital biopsy using an infrared-optical hybrid microscope.
Illinois researchers added infrared capability to a standard optical microscope, enabling digital biopsies like this one – computational “stains” without adding any dyes or chemicals to the tissue sample. Image courtesy of Rohit Bhargava

“We built the hybrid microscope from off-the-shelf components. This is important because it allows others to easily build their own microscope or upgrade an existing microscope,” said Martin Schnell, a postdoctoral fellow in Bhargava’s group and first author of the paper.

Combining the two techniques harnesses the strengths of both, the researchers said. It has the high resolution, large field-of-view and accessibility of an optical microscope. Furthermore, infrared data can be analyzed computationally, without adding any dyes or stains that can damage tissues. Software can recreate different stains or even overlap them to create a more complete, all-digital picture of what’s in the tissue.

This side-by-side comparison of a breast tissue biopsy demonstrates some of the infrared-optical hybrid microscope’s capabilities. On the left, a tissue sample dyed by traditional methods. Center, a computed stain created from infrared-optical hybrid imaging. Right, tissue types identified with infrared data. The pink in this image signifies malignant cancer. Image courtesy of Rohit Bhargava
This side-by-side comparison of a breast tissue biopsy demonstrates some of the infrared-optical hybrid microscope’s capabilities. On the left, a tissue sample dyed by traditional methods. Center, a computed stain created from infrared-optical hybrid imaging. Right, tissue types identified with infrared data. The pink in this image signifies malignant cancer. Image courtesy of Rohit Bhargava

The researchers verified their microscope by imaging breast tissue samples, both healthy and cancerous, and comparing the results of the hybrid microscope’s computed “dyes” with those from the traditional staining technique. The digital biopsy closely correlated with the traditional one.

Furthermore, the researchers found that their infrared-optical hybrid outperformed state-of-the-art in infrared microscopes in several ways: It has 10 times larger coverage, greater consistency and four times higher resolution, allowing infrared imaging of larger samples, in less time, with unprecedented detail. 

“Infrared-optical hybrid microscopy is widely compatible with conventional microscopy in biomedical applications,” Schnell said. “We combine the ease of use and universal availability of optical microscopy with the wide palette of infrared molecular contrast and machine learning. And by doing so, we hope to change how we routinely handle, image and understand microscopic tissue structure.”

The researchers plan to continue refining the computational tools used to analyze the hybrid images. They are working to optimize machine-learning programs that can measure multiple infrared wavelengths, creating images that readily distinguish between multiple cell types, and integrate that data with the detailed optical images to precisely map cancer within a sample. They also plan to explore further applications for hybrid microscope imaging, such as forensics, polymer science and other biomedical applications.

“It is very intriguing what this additional detail can offer in terms of pathology diagnoses,” Bhargava said. “This could help speed up the wait for results, reduce costs of reagents and people to stain tissue, and provide an ‘all-digital’ solution for cancer pathology.”

The National Institutes of Health supported this work. Bhargava is also affiliated with the Beckman Institute for Advanced Science and Technology and the Carle Illinois College of Medicine.

 


Liz Ahlberg Touchstone | Biomedical Sciences Editor | Illinois News Bureau

Related People

rxb

Directory

scheelinAlexander
Scheeline
bjmccallBenjamin
McCall
r-gennisRobert
Gennis
j-gerltJohn
Gerlt
sgranickSteve
Granick
mgruebelMartin
Gruebele
hergenroPaul
Hergenrother
huangRaven
Huang
mlkraftMary
Kraft
leckbandDeborah
Leckband
yi-luYi
Lu
martinisSusan
Martinis
snairSatish
Nair
eoldfielEric
Oldfield
cmsCharles
Schroeder
zanZaida
Luthey-Schulten
selvinPaul
Selvin
sksScott
Silverman
s-sligarStephen
Sligar
tajkhorsEmad
Tajkhorshid
zhao5Huimin
Zhao
pbraunPaul
Braun
mdburkeMartin
Burke
jeffchanJefferson
Chan
sdenmarkScott
Denmark
dlottDana
Dlott
foutAlison
Fout
agewirthAndrew
Gewirth
ggirolamGregory
Girolami
sohirataSo
Hirata
jainPrashant
Jain
jkatzeneJohn
Katzenellenbogen
nmakriNancy
Makri
douglasmDouglas
Mitchell
jsmooreJeffrey
Moore
murphycjCatherine
Murphy
r-nuzzoRalph
Nuzzo
dimerPhilip
Phillips
rauchfuzThomas
Rauchfuss
joaquinrJoaquín
Rodríguez-López
sarlahDavid
Sarlah
kschweizKenneth
Schweizer
jsweedleJonathan
Sweedler
vddonkWilfred
van der Donk
renskeRenske
van der Veen
vuraweisJosh
Vura-Weis
mcwhite7M.
White
sczimmerSteven
Zimmerman
beakPeter
Beak
wklemperWalter
Klemperer
jdmcdonaJ.
McDonald
pogoreloTaras
Pogorelov
mshen233Mei
Shen
dewoonDavid
Woon
wboulangWilliam
Boulanger
rxbRohit
Bhargava
qchen20Qian
Chen
jianjuncJianjun
Cheng
hy66Hong
Yang
andinomaJosé
Andino Martinez
decosteDonald
DeCoste
thhuangTina
Huang
tjhummelThomas
Hummel
dkellDavid
Kell
doctorkMichael
Koerner
marvilleKelly
Marville
crrayChristian
Ray
tlbrownTheodore
Brown
rmcoatesRobert
Coates
thdjrThom
Dunning
dykstraClifford
Dykstra
j-jonasJiri
Jonas
j-lisyJames
Lisy
shapleyJohn
Shapley
pshapleyPatricia
Shapley
zumdahl2Steven
Zumdahl
ksuslickKenneth
Suslick
jcoxJenny
Cox
sqdSean
Drummond
sheeleySarah
Sheeley
jsmaddenJoseph
Madden
cknight4Connie
Knight
schulzeHeather
Schulze
kbaumgarKeena
Finney
adkssnBeatrice
Adkisson
trabari1Katie
Trabaris
metclfKara
Metcalf
ljohnso2Lori
Johnson
lchenoweLeslie
Chenoweth
wdedoWolali
Dedo
spinnerDavid
Spinner
plblumPatricia
Simpson
stevens2Chad
Stevens
lsagekarLori
Sage-Karlson
bertholdDeborah
Berthold
kecarlsoKathryn
Carlson
sdesmondSerenity
Desmond
axelson2Jordan
Axelson
scbakerStephanie
Baker
pflotschPriscila
Falagan Lotsch
dgrayDanielle
Gray
thennes2Tom
Hennessey
holdaNancy
Holda
aibarrAlejandro
Ibarra
kimshSung Hoon
Kim
kocherg2Nikolai
Kocherginsky
philipk2Philip
Kocheril
legare2Stephanie
Legare
alewandoAgnieszka
Lewandowska
smccombiStuart
McCombie
jdm5Justin
McGlauchlen
egmooreEdwin
Moore
myerscouKathleen
Myerscough
snalla2Siva
Nalla
romanovaElena
Romanova
roubakhiStanislav
Rubakhin
shvedalxAlexander
Shved
asoudaAlexander
Soudakov
xywangXiying
Wang
kwilhelKaren
Wilhelmsen
wilkeyRandy
Wilkey
silongSilong
Zhang
schlembaMary
Schlembach
trimmellAshley
Trimmell
emccarr2Elise
McCarren
cmercierChristen
Mercier
atimpermAaron
Timperman
niesShuming
Nie
hshanHee-Sun
Han
mmgMutha
Gunasekera
kknightsKatriena
Knights
lisawLisa
Williamson
keinckKatie
Einck
kneef1Kate
Neef
txiang4Tiange
Xiang
j-hummelJohn
Hummel
i-paulIain
Paul
munjanjaLloyd
Munjanja
glnGayle
Nelsen
agerardAnna
Gerard
powerskaKimberly
Powers
lolshansLisa
Olshansky
miricaLiviu
Mirica
qingcao2Qing
Cao
lisa3Lisa
Johnson
tinalambTina
Lamb
baronpBaron
Peters
bransle2Sarah
Bransley
dylanmh2Dylan
Hamilton
raegansRaegan
Smith
apm8Angad
Mehta
leverittJohn
Leveritt
xingwXing
Wang
emillrEva
Miller
jmill24Jacqueline
Miller
jlbass2Julia
Bass
ramonarRamona
Rudzinski
tlcraneTracy
Crane
cejohnstCelia
Johnston
adlAmber
LaBau