Skip to main content

A sample-preparation-free, automated, sample-to-answer system for cell counting in human body fluids

Abstract

While many clinical laboratory tests are now highly automated, body fluid cell counting, particularly in low-cellularity samples such as cerebral spinal fluid (CSF), is often performed manually. Here, we report a simple, cost-effective method to obtain white and red blood cell counts from human body fluids such as CSF. The method consists of a compact, automated, and low-cost fluorescence microscope system, coupled to a sample chamber containing all of the necessary reagents in dry form to stain and prepare the sample. Sample focus and scanning are handled automatically, and the acquired multimodal images are automatically analyzed to extract cell counts. Comparison with manual counting on over 200 clinical samples shows excellent agreement. As the system counts a substantially larger image region than a standard manual cell count, we find our sensitivity to extremely low cellularity samples to potentially be higher than the manual gold standard, evidenced by our system recording images of cells in samples whose cell count was registered as “0” by a trained user. Thus, our system holds promise for routine, automated, and sensitive analysis of body fluids whose cellularity extends across a wide dynamic range.

Graphical abstract

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. CLSI. Body fluid analysis for cellular composition; approved guidelines. CLSI document H56-A.Wayne. Clinical and Laboratory Standard Institute: PA; 2006.

    Google Scholar 

  2. Sandhaus LM. Is the hemocytometer obsolete for body fluid cell counting? Am J Clin Path. 2016;145(3):294–5.

    Article  Google Scholar 

  3. Martín MJA, Queral LA, Frías LS, Amado LV, Merino A, de Guadiana-Romualdo LG. Automated cell count in body fluids: a review. Adv Lab Med. 2021;20210011.

  4. Buoro S, Peruzzi B, Fanelli A, Seghezzi M, Manenti B, Lorubbio M, et al. Two-site evaluation of the diagnostic performance of the Sysmex XN Body Fluid (BF) module for cell count and differential in cerebrospinal fluid. Int J Lab Hematol. 2018;40(1):26–33.

    CAS  Article  Google Scholar 

  5. Buoro S, Seghezzi M, Dominoni P, Moioli V, Manenti B, Previtali G, et al. Lack of harmonization in high fluorescent cell automated counts with body fluids mode in ascitic, pleural, synovial, and cerebrospinal fluids. Int J Lab Hematol. 2019;41(2):277–86.

    Article  Google Scholar 

  6. Seo JY, Lee S-T, Kim S-H. Performance evaluation of the new hematology analyzer Sysmex XN-series. Int J Lab Hematol. 2015;37(2):155–64.

    CAS  Article  Google Scholar 

  7. Huang W-H, Lu L-P, Wu K, Guo F-Y, Guo J, Yu J-L, et al. Extent of agreement between the body fluid model of Sysmex XN-20 and the manual microscopy method. J Clin Lab Anal. 2017;31(5):e22101.

    Article  Google Scholar 

  8. Zhu H, Sencan I, Wong J, Dimitrov S, Tseng D, Nagashima K, et al. Cost-effective and rapid blood analysis on a cell-phone. Lab Chip. 2013;13(7):1282–8.

    CAS  Article  Google Scholar 

  9. Smith ZJ, Gao T, Chu K, Lane SM, Matthews DL, Dwyre DM, et al. Single-step preparation and image-based counting of minute volumes of human blood. Lab Chip. 2014;14(16):3029–36.

    CAS  Article  Google Scholar 

  10. Forcucci A, Pawlowski ME, Majors C, Richards-Kortum R, Tkaczyk TS. All-plastic, miniature, digital fluorescence microscope for three part white blood cell differential measurements at the point of care. Biomed Opt Express. 2015;6(11):4433–46.

    Article  Google Scholar 

  11. Powless A, Feekin L, Hutcheson J, Alapat D, Muldoon T. Low-cost computing and network communication for a point-of-care device to perform a 3-part leukocyte differential. Proc. SPIE 9715; 2016.

  12. Xie D, Xie Y, Liu P, Tong L, Hu C, Shao P, et al. Performance of a cost-effective and automated blood counting system for resource-limited settings operated by trained and untrained users. J Biophotonics. 2018;11(2):e201700030.

    Article  Google Scholar 

  13. Li X, Deng Q, Liu H, Lei Y, Fan P, Wang B, et al. A smart preparation strategy for point-of-care cellular counting of trace volumes of human blood. Anal Bioanal Chem. 2019;411(13):2767–80.

    CAS  Article  Google Scholar 

  14. Lv M, Zhao X, Chen F, Yu M, Li C, Sun J. A rapid white blood cell classification system based on multimode imaging technology. J Biophotonics. 2020;13(11):e202000197.

    CAS  Article  Google Scholar 

  15. Chen Y, Chen X, Li M, Fan P, Wang B, Zhao S, et al. A new analytical platform for potential point-of-care testing of circulating tumor cells. Biosens Bioelectron. 2021;171:112718.

    CAS  Article  Google Scholar 

  16. Bachar N, Benbassat D, Brailovsky D, Eshel Y, Glück D, Levner D, et al. An artificial intelligence-assisted diagnostic platform for rapid near-patient hematology. medRxiv; 2021.

  17. Chen X, Luo P, Hu C, Yan S, Lu D, Li Y, et al. Nanometer precise red blood cell sizing using a cost-effective quantitative dark field imaging system. Biomed Opt Express. 2020;11(10):5950–66.

    CAS  Article  Google Scholar 

  18. Li Y, Zheng R, Wu Y, Chu K, Xu Q, Sun M, et al. A low-cost, automated parasite diagnostic system via a portable, robotic microscope and deep learning. J Biophotonics. 2019;12(9):e201800410.

    PubMed  Google Scholar 

  19. Gao T, Smith ZJ, Lin T-Y, Carrade Holt D, Lane SM, Matthews DL, et al. Smart and fast blood counting of trace volumes of body fluids from various mammalian species using a compact, custom-built microscope cytometer. Anal Chem. 2015;87(23):11854–62.

    CAS  Article  Google Scholar 

  20. Powless AJ, Prieto SP, Gramling MR, Conley RJ, Holley GG, Muldoon TJ. Evaluation of acridine orange staining for a semi-automated urinalysis microscopic examination at the point-of-care. Diagnostics. 2019;9(3).

  21. Campbell RA, Eifert RW, Turner GC. OpenStage: a low-cost motorized microscope stage with sub-micron positioning accuracy. PLoS One. 2014;9(2):e88977.

    Article  Google Scholar 

  22. Lu Q, Liu G, Xiao C, Hu C, Zhang S, Xu RX, et al. A modular, open-source, slide-scanning microscope for diagnostic applications in resource-constrained settings. PLoS One. 2018;13(3):e0194063.

    Article  Google Scholar 

  23. Yan Z, Chen G, Xu W, Yang C, Lu Y. Study of an image autofocus method based on power threshold function wavelet reconstruction and a quality evaluation algorithm. Appl Opt. 2018;57(33):9714–21.

    Article  Google Scholar 

  24. Kim YR, Ornstein L. Isovolumetric sphering of erythrocytes for more accurate and precise cell volume measurement by flow cytometry. Cytometry. 1983;3(6):419–27.

    CAS  Article  Google Scholar 

  25. Traganos F, Darzynkiewicz Z, Sharpless T, Melamed MR. Simultaneous staining of ribonucleic and deoxyribonucleic acids in unfixed cells using acridine orange in a flow cytofluorometric system. J Histochem Cytochem. 1977;25(1):46–56.

    CAS  Article  Google Scholar 

  26. Chen Z, Wang Y, Zeng A, Chen L, Wu R, Chen B, et al. The clinical diagnostic significance of cerebrospinal fluid d-lactate for bacterial meningitis. Clin Chim Acta. 2012;413(19):1512–5.

    CAS  Article  Google Scholar 

  27. Veerman A, Huismans L, Zantwijk I. Storage of cerebrospinal fluid samples at room temperature. Acta Cytol. 1985;29:188–9.

    CAS  PubMed  Google Scholar 

  28. Powless A, Conley R, Freeman K, Muldoon T. Considerations for point-of-care diagnostics: evaluation of acridine orange staining and postprocessing methods for a three-part leukocyte differential test. J Biomed Opt. 2017;22(3):035001.

    Article  Google Scholar 

  29. de Jonge R, Brouwer R, de Graaf MT, Luitwieler RL, Fleming C, de Frankrijker-Merkestijn M, et al. Evaluation of the new body fluid mode on the Sysmex XE-5000 for counting leukocytes and erythrocytes in cerebrospinal fluid and other body fluids. Clin Chem Lab Med. 2010;48(5):665–75.

    Article  Google Scholar 

Download references

Funding

This research was funded by the Ministry of Science and Technology of China’s National Key Research and Development Program, Grant number 2016YFA0201300 (ZJS) and the Chongqing Municipal Science and Technology Commission, grant number cstc2017shmsA130083 (HD, ZJS), whose support is gratefully acknowledged.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Hu Dou or Zachary J. Smith.

Ethics declarations

Ethics approval

This study was approved by the Ethics Committee of the Children’s Hospital of Chongqing Medical University, approval number:(2016)年伦审(研)第(67)号 (Translation: 2016 IRB Research approval number 67).

Source of biological material

The samples utilized in this study were anonymized discarded patient samples from the Clinical Laboratory of the Children’s Hospital of Chongqing Medical University, which were not collected for the purposes of this study.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

ESM 1

(PDF 668 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lu, Q., Chu, K., Dou, H. et al. A sample-preparation-free, automated, sample-to-answer system for cell counting in human body fluids. Anal Bioanal Chem 413, 5025–5035 (2021). https://doi.org/10.1007/s00216-021-03466-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00216-021-03466-6

Keywords

  • Clinical/biomedical analysis
  • Microscopic imaging
  • Point of care testing
  • Cell counting