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CC BY-NC-ND 4.0 · Journal of Morphological Sciences 2015; 32(02): 078-081
DOI: 10.4322/jms.080114
Original Article
Thieme Revinter Publicações Ltda Rio de Janeiro, Brazil

A reproducible differential blood cell count without a cytometer or specific markers

P. B. Tozetti
1   Masters Program in Pharmaceutical Sciences, Universidade de Vila Velha - UVV, Rua Comissário José Dantas de Melo, 21, Boa Vista, CEP 29102-770, Vila Velha, ES, Brazil
,
F. E. Pinto
1   Masters Program in Pharmaceutical Sciences, Universidade de Vila Velha - UVV, Rua Comissário José Dantas de Melo, 21, Boa Vista, CEP 29102-770, Vila Velha, ES, Brazil
,
E. Pereira Neto
2   Masters Program in Animal Sciences, Universidade Vila Velha - UVV, Rua Comissário José Dantas de Melo, 21, Boa Vista, CEP 29102-770, Vila Velha, ES, Brazil
,
T. U. Andrade
1   Masters Program in Pharmaceutical Sciences, Universidade de Vila Velha - UVV, Rua Comissário José Dantas de Melo, 21, Boa Vista, CEP 29102-770, Vila Velha, ES, Brazil
,
D. C. Endringer
1   Masters Program in Pharmaceutical Sciences, Universidade de Vila Velha - UVV, Rua Comissário José Dantas de Melo, 21, Boa Vista, CEP 29102-770, Vila Velha, ES, Brazil
,
D. Lenz
1   Masters Program in Pharmaceutical Sciences, Universidade de Vila Velha - UVV, Rua Comissário José Dantas de Melo, 21, Boa Vista, CEP 29102-770, Vila Velha, ES, Brazil
› Author Affiliations
Further Information

Publication History

15 August 2014

07 October 2015

Publication Date:
09 October 2018 (online)

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Abstract

Introduction: A differential blood cell count represents a substantial component of the daily clinical routine. The aim of this study was to conduct automated high-throughput analyses of light microscopy images of leukocytes with unspecific staining (hematoxylin-eosin, HE) and to classify the resulting subgroups (lymphocytes, monocytes and neutrophils). Materials and Method: The software package CellProfiler was used for the image analysis and cell classification. The results were compared to those from hematological laboratories that analyzed the same samples. Results: There was a high similarity between the results obtained by image analysis and those from the hematological laboratories, with an r2=0.95 for the differential count and an r2=0.89 for the total count. Conclusion: Differential blood cell count using light microscopy, nonspecific dyes and advanced image analysis is accurate, reproducible, and less expensive compared to other techniques. The results also showed a strong correlation when compared to traditional methods.

Keywords

cytometry - blood cell count - high-throughput analyses - image analyses - machine learning