“VIRTUAL FLOW CYTOMETRY” OF IMMUNOSTAINED LYMPHOCYTES ON MICROSCOPIC TISSUE SLIDES: Hernani D. Cualing MD, Lynn Moscinski MD*.

•*H Lee Moffitt Cancer Center & Research Institute, University of South Florida, Tampa, FL, USA.•Acknowledgements: Eric Zhong, Mantle Cell Lymphoma Task Force(J Tao, E Sotomayor, S Dessureault…), H Molina, J Balasi, G Shaheen HISTOLOGY FLOW CYTOMETRY, MOFFITT)

 

ABSTRACT

BACKGROUND: A method and approach was developed for fully automated measurements of immunostained lymphocytes in tissue sections by means of digital color microscopy and patent pending advanced cell analysis. The validation data for population statistic measurements of immunostained lymphocytes in tissue sections using tissue cytometry is presented.

DESIGN: Segmentation of a 512 x 474 RGB image and display of statistical results table took 12 to 15 seconds using proprietary developed algorithms. We used a panel of 7 antibodies for validation on 14 cases of mantle cell lymphoma (MC) giving percentage positive, total lymphocytes, density staining. An total of 2,027 image frames with 810,800 cell objects were evaluated. Antibodies to CD3, CD4, CD8, Bcl-1, Ki-67, CD20, CD5 were subjected to virtual flow cytometry on tissue. The results of tissue cytometry were compared with manual counts of expert observers and with the results of flow cytometry immunophenotyping of the same specimen.

RESULTS: The correlation coefficient and 95 % confidence interval by linear regression analysis yielded a high concordance between manual human results (M), flow cytometry results(FC), and tissue cytometry (TC) results per antibody, (r =0.9365 manual vs TC, r =0.9537 FC vs TC). We noted a lower CD8 tumor infiltrating lymphocytes in blastic form than in non-blastic form of mantle cell and a higher Ki67 in the former subtype than in the latter.

CONCLUSION: These results suggest the new technology of Tissue Cytometry could be a clinically valid surrogate for both manual and flow cytometry analysis when only tissue immunohistochemistry(IHC) is available for diagnosis and prognosis. The application for cancer diagnosis, monitoring, and prognosis is for objective, rapid, automated counting of immunostained cells in tissues with percentage results. These results could be also be used in standardization of cut off percentage used in diagnosis; for exemplary tool for leukemic blasts(CD34) >20% for acute leukemia, for determining automated 30 % cut off for positivity for prognostic markers in diffuse large B-cell lymphoma for bcl-2, bcl-6, Mum-1, and CD10.

INTRODUCTION

We developed a method and apparatus that perform image analysis of immunohistochemically stained tissue. The objective was to recreate the functionality of a flow cytometer, but instead of using the requisite cell suspension specimen, was modified to be applied directly on stained microscopic slides with tissue sections. The hematopathologists standard of clinical practice in most of patient’s reports is to estimate the percentage of immunohistochemically stained cells and report the visual or manual estimate. This practice is subjective and often gives wide range of results that depends on the level of the microscopists’ skill. This is due to the difficulty in counting positive cells accurately because of overlapped stained nuclei, variability of staining, and the limitation of our visual system. A significant number of hematology cases are fixed and embedded in paraffin and are not amenable to flow cytometry analysis which requires fresh cell suspension of tissue. Routinely in pathology practice, a panel of 5 to 15 antibodies in average are applied to the slide based tissue sections to create a differential matrix to rule in or out a diagnosis based on the tumor associated markers. The role of these markers extend beyond ancillary and often takes center stage in accurate diagnosis. The use of IHC may shift the diagnostic probability from 75 % to 100%. This is especially true in hemato-pathology diagnosis where an enhanced diagnostic accuracy is reported if the immunologic results are included ( Blood, Armitage Int Lymphoma study, 1997). The enhanced accuracy was across the board and was noted from 5 to 35 % of the cases. Yet currently, there is yet no system commercially available that will do flow cytometry results on paraffin embedded tissue immunohistochemically stained sections; hence the motivation to tackle this difficult challenge. We applied a novel, useful, and accurate algorithm based on population or cell-based approach instead of the more common pixel or area-based approach used by many current research imaging algorithms to come up with results similar to flow cytometry dot plot histograms. These results, along with a table, are familiar and provide an objective percent positive and negative counts of tissue immunohistochemistry to the diagnostic pathologists and hematologist-oncologists and enhance accuracy of diagnosis, disease monitoring, and prognostication.

RESULTS : When compared with manual and flow cytometry results, the concordance of Tissue Cytometry is high. The results of tissue cytometry were compared with manual counts of expert observers and with the results of flow cytometry immunophenotyping of the same specimen. The correlation coefficient and 95 % confidence interval by linear regression analysis yielded a high concordance between manual human results (M), flow cytometry results(FC), and tissue cytometry (TC) results per antibody, (r =0.9365 manual vs TC, r =0.9537 FC vs TC). We noted a lower CD8 tumor infiltrating lymphocytes in blastic form than in non-blastic form of mantle cell and a higher Ki67 in the latter subtype than in the former. Technically oriented images, histograms, image analysis results are presented on the right panels including the regression analysis figures.

DESIGN

MATERIALS: Paraffin embedded material of lymphocyte rich tissue was used for this study. We used 7 monoclonal antibodies for validation on 14 cases with tissue biopsy of the excised lymph nodes obtained for diagnosis of lymphoma. A total of 2,027 image frames with 810,800 cell objects were evaluated. Membrane reactive antibodies to T cell associated markers CD3, CD4, CD5, and CD8 were used. In addition, B cell reactive monoclonal antibody to CD20 ( L26, mature B cells) was used. These antibodies were run in flow cytometry and correlated with tissue immunoreactivity using virtual flow cytometry on tissue. Nuclear reactive antibodies to Bcl-1(cyclin D1, mantle cell lymphoma and others) and Ki-67 (Mib-1, proliferative marker) were analysed by tissue cytometry only. METHODS: Flow cytometry analysis was done using logical gating on dual CD20 and CD5 positive cells counting 5,000 events. Becton Dickinson FacsCalibur with CellQuest software version 3.2.1 four color acquisition and analysis. Membrane reactive antibodies to T cell associated markers/clone names with fluorophores are CD3 PerCP-Cy5.5 ( SK7), CD4 FITC ( SK3), CD5 APC (L17F12), and CD8 APC (SK1) were used. CD20 APC ( L27, mature B cells) and CD71 PE (YDJ1.2.2 ) was used. The manual estimates of immunoreactivity were compared with the results of cell suspension flow cytometry results and with the results of virtual flow cytometry on each of the corresponding series of tissue obtained for diagnosis. Both the positively stained cells and the unstained nuclei of the same type of cells are sequentially extracted to yield a numerator and denominator to calculate percent positive and total cells. Stain density is also obtained per cell and correlated with size. The pixel size was converted to microns and the density spread from 0 to 255 where negative cell events are plotted blue and positive cell events plotted red. Positivity criteria was determined by novel automated thresholding-visual concordance subroutine. The slides were examined using a Leica brightfield microscope, objective 20x equipped with Insight color CCD(Diagnostic Instruments, Inc,Westlar Germany), version 3.4 for Windows NT 2000. The camera chip photoreceptive field (1060 x 1020 pixels) was by software-mode trimmed to 512 x 474 pixels for dimensionality reduction, object size optimum visualization, and for computational efficiency. These images were stored as either a JPEG or PICS file and analysed using an previously developed advanced cell imaging software. No manual or interactive labeling or shading or color correction was performed. The light intensity rheostat was set to 7.0 of 12.0. The light source was 30 watt 12 v incandescent bulb with a condenser blue filter 80a Tiffen, with condenser aperture set a 0.5 ph, under Kohler illumination, and using a 20x nplan 0.4 NA objective. The camera has a single CCD with interpolated R, G, B filters overlying each substrate pixel was used. The pixel size for the 20x image frame of 512 x 474 pixels corresponded to 1.5 pixels per micron. A lymphocyte nuclei averaged 12-15 microns in nuclear diameter but since these nuclei were tangentially cut in a random fashion by the microtome, the range is noted from 5 to 35 microns in tangent diameters. The image was manually focused, captured, and saved. Each image file was 711 Kbytes. Each image frame took from12-15 seconds from start of analysis to statistical table and correlated dot plot histogram result generation.

CORRELATED IMMUNOSTAINED IMAGES AND CORRESPONDING DOT PLOT TISSUE CYTOMETRY RESULTS (CELL SIZE IN MICRONS VS STAINING DENSITY)

Color image frame of CD8 + immunostained tumor infiltrating cell response and mantle cell lymphoma(Brown DAB and Blue Hematoxylin, 20x).

Segmented brown stained CD8+ lymphocytes along with the unstained blue nuclei of non-CD8 tumor MC lymphocytes

RESULTS OF TISSUE CYTOMETRY ON CD8 STAINING

Table showing Staining Density, Total Cell Population, Positive Cells, Percentage Positive Cells. (1 run in 1case table output formatted as a batch mode )

CONCLUSION

We applied a recursive thresholding algorithm to segment intensity histograms from red and blue channels of image in RGB as a preprocessing step in segmentation of chromogen-stained cells. We found that this approach along with cellular logic is able to accurately segment optimally stained cells with appropriate counterstain( for brown DAB-chromogen-immunostained cells and blue hematoxylin-stained non-immunoreactive cells).

Given an array of parameters that could be used to automate counting of positively stained cells, we found this approach of using density and color and size only information is able do population statistics and convert the tissue immunostaining image to computerized data as a table of results and dot plot two-dimensional histogram results with reliable, robust, accurate reporting similar to that of a flow cytometer.

We differ from the current methods of image analysis systems by focusing on cell-based population statistics instead of pixel-area data. The correlation between each case run in flow cytometry and estimated by experts and by the advanced image Tissue Cytometry is high and suggest a valid approach to objectively quantifying immunostaining of lymph nodes in tissue by a defined set of monoclonal antibodies useful in lymphoma diagnosis, monitoring and prognosis.

REFERENCES

Cualing H. Automated Analysis in Flow Cytometry. Cytometry, 2000 , 42:p.110-113.

Young IT, Quantitative Microscopy. IEEE Engineering in Medicine and Biology, 1996, 15(1): p.59-66.

Ridler TW ,Calvard S. Picture Thresholding Using Iterative Selection Method. IEEE Trans. On Systems, Man, and Cybernetics, 1978. SMC-8(8):p 630-632.

Cualing H. Kothari R, Balachander T. Immunophenotypic Diagnosis of Acute Leukemia Using Decision Tree Induction. Lab Investigation, 1999, 79:p.205-212.

Positive brown cells segmented below (The numerator)

Negative cells below are added to positive cells to give the total cells ( The denominator )

CD3

Bcl-1

Ki-67

CD5

TISSUE CYTOMETRY: SIZE VS STAINING DENSITY (LINEAR)

Cell Size in microns in Y and the Staining Density in X( 0 to 255, where blue stain is close to zero and brown stain is towards 255)

Correlation of flow cytometry(flow) cell suspension fluorescent analysis with manual estimate of immunostaining results and with the computerized Tissue Cytometer (TC)

r = 0.95 r = 0.93

FLOW CYTOMETRY: SIZE(FSC) VS CD8 FLUORESCENCE INTENSITY(LINEAR )

6 % CD8 +

85%

24%

28%

133/480

99/422

311/367

357/644

55%