Abstract

Immunohistochemistry (IHC) plays an important role in target protein analysis. However, many researchers analyze IHC images by five/three-tier manual ranking methods based on stained area and density. Such manual scoring might be biased by the antibody amount, counterstaining density, overall brightness, and most importantly, researchers' ranking experience. The potential lack of reliability in manual approach drives us to develop an automatic tool to quantitatively analyze IHC, which can also be used for immunocytochemistry (ICC). We applied a “color deconvolution” method based on an red-green-blue (RGB) color vector matching the color of desired immunochemistry agent, 3,3′-diaminobenzidine (DAB) with haematoxylin in this case, to acquire pseudo-color images. Subsequently, Density, the product of integrating the single pixel staining density by area stained, is used as an index of immunostaining. We observed a strong correlation between the results by our automatic method and the manual scoring from experienced researchers, demonstrating the utility of this method in IHC and ICC. For IHC analysis, five-tier ranking based on density (n = 161) shows a high Spearman's coefficient (rho) of 0.80 (P < 0.0001) with the annotation given by two experienced scientists. However, the rho between experienced and inexperienced researchers' annotation (n = 154) is only 0.66 (P < 0.0001). In immunocytochemistry, the rho between density and experienced researchers' annotation is 0.80 (n = 44, P < 0.0001). In conclusion, our method can rank multiple protein targets in immunohistochemistry and may be also used in immunochemistry.

References

References
1.
Ramos-Vara
,
J. A.
, and
Miller
,
M. A.
,
2014
, “
When Tissue Antigens and Antibodies Get Along: Revisiting the Technical Aspects of Immunohistochemistry–The Red, Brown, and Blue Technique
,”
Vet. Pathol.
,
51
(
1
), pp.
42
87
.10.1177/0300985813505879
2.
Heyderman
,
E.
,
1980
, “
The Role of Immunocytochemistry in Tumour Pathology: A Review
,”
J. R. Soc. Med.
,
73
(
9
), pp.
655
658
.10.1177/014107688007300912
3.
Matos
,
L. L.
,
Trufelli
,
D. C.
,
de Matos
,
M. G.
, and
da Silva Pinhal
,
M. A.
,
2010
, “
Immunohistochemistry as an Important Tool in Biomarkers Detection and Clinical Practice
,”
Biomark Insights
,
5
, pp.
9
20
.10.4137/bmi.s2185
4.
Fedchenko
,
N.
, and
Reifenrath
,
J.
,
2014
, “
Different Approaches for Interpretation and Reporting of Immunohistochemistry Analysis Results in the Bone Tissue—A Review
,”
Diagn. Pathol.
,
9
(
1
), p.
221
.10.1186/s13000-014-0221-9
5.
Cohen
,
D. A.
,
Dabbs
,
D. J.
,
Cooper
,
K. L.
,
Amin
,
M.
,
Jones
,
T. E.
,
Jones
,
M. W.
,
Chivukula
,
M.
,
Trucco
,
G. A.
, and
Bhargava
,
R.
,
2012
, “
Interobserver Agreement Among Pathologists for Semiquantitative Hormone Receptor Scoring in Breast Carcinoma
,”
Am. J. Clin. Pathol.
,
138
(
6
), pp.
796
802
.10.1309/AJCP6DKRND5CKVDD
6.
Rimm
,
D. L.
,
2006
, “
What Brown Cannot Do for You
,”
Nat. Biotechnol.
,
24
(
8
), pp.
914
916
.10.1038/nbt0806-914
7.
Ruifrok
,
A. C.
, and
Johnston
,
D. A.
,
2001
, “
Quantification of Histochemical Staining by Color Deconvolution
,”
Anal. Quant. Cytol. Histol.
,
23
(
4
), pp.
291
299
.https://pubmed.ncbi.nlm.nih.gov/11531144/#:~:text=The%20color%20deconvolution%20algorithm%20resulted,saturation%20of%20staining%20was%20prevented.
8.
Schindelin
,
J.
,
Arganda-Carreras
,
I.
,
Frise
,
E.
,
Kaynig
,
V.
,
Longair
,
M.
,
Pietzsch
,
T.
,
Preibisch
,
S.
,
Rueden
,
C.
,
Saalfeld
,
S.
,
Schmid
,
B.
,
Tinevez
,
J. Y.
,
White
,
D. J.
,
Hartenstein
,
V.
,
Eliceiri
,
K.
,
Tomancak
,
P.
, and
Cardona
,
A.
,
2012
, “
Fiji: An Open-Source Platform for Biological-Image Analysis
,”
Nat. Methods
,
9
(
7
), pp.
676
682
.10.1038/nmeth.2019
9.
Schoonjans
,
F.
,
Zalata
,
A.
,
Depuydt
,
C. E.
, and
Comhaire
,
F. H.
,
1995
, “
MedCalc: A New Computer Program for Medical Statistics
,”
Comput. Methods Programs Biomed.
,
48
(
3
), pp.
257
262
.10.1016/0169-2607(95)01703-8
10.
Walt
,
S. V. D.
,
Colbert
,
S. C.
, and
Varoquaux
,
G.
,
2011
, “
The NumPy Array: A Structure for Efficient Numerical Computation
,”
Comput. Sci. Eng.
,
13
(
2
), pp.
22
30
.10.1109/MCSE.2011.37
11.
Cardinale
,
J.
,
Paul
,
G.
, and
Sbalzarini
,
I. F.
,
2012
, “
Discrete Region Competition for Unknown Numbers of Connected Regions
,”
IEEE Trans. Image Process
,
21
(
8
), pp.
3531
3545
.10.1109/TIP.2012.2192129
12.
Varghese
,
F.
,
Bukhari
,
A. B.
,
Malhotra
,
R.
, and
De
,
A.
,
2014
, “
IHC Profiler: An Open Source Plugin for the Quantitative Evaluation and Automated Scoring of Immunohistochemistry Images of Human Tissue Samples
,”
PLoS One
,
9
(
5
), p.
e96801
.10.1371/journal.pone.0096801
13.
Anagnostou
,
V. K.
,
Lowery
,
F. J.
,
Syrigos
,
K. N.
,
Cagle
,
P. T.
, and
Rimm
,
D. L.
,
2010
, “
Quantitative Evaluation of Protein Expression as a Function of Tissue Microarray Core Diameter: Is a Large (1.5 mm) Core Better Than a Small (0.6 mm) Core?
,”
Arch. Pathol. Lab Med.
,
134
(
4
), pp.
613
619
.10.1043/1543-2165-134.4.613
14.
Weberpals
,
J. I.
,
Amin
,
M. S.
,
Chen
,
B. E.
,
Tu
,
D.
,
Spaans
,
J. N.
,
Squire
,
J. A.
,
Eisenhauer
,
E. A.
,
Virk
,
S.
,
Ma
,
D.
,
Duciaume
,
M.
,
Hoskins
,
P.
, and
LeBrun
,
D. P.
,
2016
, “
First Application of the Automated QUantitative Analysis (AQUA) Technique to Quantify PTEN Protein Expression in Ovarian Cancer: A Correlative Study of NCIC CTG OV.16
,”
Gynecol. Oncol.
,
140
(
3
), pp.
486
493
.10.1016/j.ygyno.2016.01.015
15.
Welsh
,
A. W.
,
Moeder
,
C. B.
,
Kumar
,
S.
,
Gershkovich
,
P.
,
Alarid
,
E. T.
,
Harigopal
,
M.
,
Haffty
,
B. G.
, and
Rimm
,
D. L.
,
2011
, “
Standardization of Estrogen Receptor Measurement in Breast Cancer Suggests False-Negative Results Are a Function of Threshold Intensity Rather Than Percentage of Positive Cells
,”
J. Clin. Oncol.
,
29
(
22
), pp.
2978
2984
.10.1200/JCO.2010.32.9706
16.
Lupidi
,
M.
,
Coscas
,
F.
,
Cagini
,
C.
,
Fiore
,
T.
,
Spaccini
,
E.
,
Fruttini
,
D.
, and
Coscas
,
G.
,
2016
, “
Automated Quantitative Analysis of Retinal Microvasculature in Normal Eyes on Optical Coherence Tomography Angiography
,”
Am. J. Ophthalmol.
,
169
, pp.
9
23
.10.1016/j.ajo.2016.06.008
17.
Webster
,
J. D.
, and
Dunstan
,
R. W.
,
2014
, “
Whole-Slide Imaging and Automated Image Analysis: Considerations and Opportunities in the Practice of Pathology
,”
Vet. Pathol.
,
51
(
1
), pp.
211
223
.10.1177/0300985813503570
18.
Sharpe
,
L. T.
,
Stockman
,
A.
,
Jagla
,
W.
, and
Jägle
,
H.
,
2005
, “
A Luminous Efficiency Function, V*(λ), for Daylight Adaptation
,”
J. Vision
,
5
(
11
), p.
3
.10.1167/5.11.3
19.
van der Loos
,
C. M.
,
2008
, “
Multiple Immunoenzyme Staining: Methods and Visualizations for the Observation With Spectral Imaging
,”
J. Histochem. Cytochem.
,
56
(
4
), pp.
313
328
.10.1369/jhc.2007.950170
You do not currently have access to this content.