A Simple Approach for Counting CD4+ T Cells Based on a Combination of Magnetic Activated Cell Sorting and Automated Cell Counting Methods
and Huong Thi Thu Pham 1,*Round 1
Reviewer 1 Report
This manuscript established an efficient method of CD4+ T cell separation using magnetic activated cell sorting and optimized automated cell-counting system CountessTM. This method provides a simple and low-cost model for CD4+ T cell diagnose. The subject is very practical and the paper is well organized. The introduction gave a satisfactory literature survey on the similar topic. The proposed method was outlined well. Appropriate figures were given to make the paper understood easily. I think that the paper is publishable in the applied sciences after minor revision. I have a minor query. Is there comparison about viability and completeness of sorted cells by MACS and FACS? It would be better to add in discussion.
Author Response
Point 1: Is there comparison about viability and completeness of sorted cells by MACS and FACS? It would be better to add in discussion.
Answer 1: Thank you for your question.
Cell viability should be determined for further cell culture purpose, but not for T CD4+ cell counting in blood. As a result, neither the gold-standard FACS nor the MACS methods were designed to detect T CD4+ cell viability.
In order to assess the completeness of MACS-sorted cells, we used the FACS method to determine the percentage population of CD4+ T cells in the samples following MACS sorting. The data in Fig.1b and Fig.2b reveal that NP-CD4 successfully isolated CD4+ T cells from blood samples, with a proportion of targeted cells in the sample exceeding 95%. Regarding FACS method, CD4+ T cells were labeled with anti-CD3 and anti-CD4 antibodies for analysis by gating their fluorescent signals. As the cells were not isolated from other blood cells by FACS method, we did not compare the completeness of sorted cells between the two methods.
Author Response File: 
Author Response.docx
Reviewer 2 Report
In this article Ngoc Duc Vo and colleagues describe a system for Counting CD4 + T Cells Based on a combination of magnetic activated cell sorting and Automated cell counting methods. This method could present many advantages because it turns out to be quick and easy. Moreover, compared to the conventional method requiring 4 ml blood input, the present MACS procedure enables the separation of CD4 + T cells directly from 50 µl of blood, making the method friendly to patients. Further, the optimized procedure does not require a centrifugation process, resulting in a considerably shorter time and less labor for performing, while greatly reducing the risk of sample contamination to the environment. This methodology is cost-effective, with relatively good accuracy and good reliability.
Therefore, I have some observations that needed to be addressed for the paper publication.
Minor points:
- it might be interesting to increase the number of blood samples analyzed;
- calculate positive predictive value (PPV), and negative predictive value (NPV);
- the authors should make an ROC curve to evaluate the sensitivity and specificity.
Author Response
Point 1: it might be interesting to increase the number of blood samples analyzed.
Answer 1: We understand the reviewer’s concern with the small sample size (n=48) in our study; however, we tried our best to collect blood samples from healthy donors from hospitals and to conduct experiments between lock-down times that happened over the study's timeframe. Although the sample size of this main study is small, statistical analysis and comparisons to established methods such as the gold standard FACS analysis and the commercial available PIMA analyzer show that the data acquired is reliable. As a result, we expect that our developed assay will have an impact on readers.
Point 2: calculate positive predictive value (PPV), and negative predictive value (NPV).
Answer 2:
The following indexes of sensitivity, specificity, upward misclassification rate (UMR), downward misclassification rate (DMR), PPV, and NPV have been calculated theoretically based on the components TP (True positives), TN (True negatives), FP (False positives), and FN (False negatives), as follows:
|
We have discussed the sensitivity, specificity, UMR and DMR in our manuscript on discussion part (page 11, line 397 to 407 on applsci-1391960). Following the reviewer’s comments, we have additionally calculated the two factors of PPV and NPV. In Table 2, these values have been added in comparison to the values obtained by PIMA method.
Table 2. Sensitivity, specificity, PPV, NPV and misclassification rate of MACS-Countess to the reference FASC at cut-off 350 and 500 cells/µl.
|
Method |
Cut-off |
Sensitivity |
Specificity |
PPV |
NPV |
Upward misclassification rate |
Downward misclassification rate |
|
MACS-Countess |
350 cells/µl |
89.7% |
94.7% |
96.3% |
85.7% |
10.3% |
5.3% |
|
500 cells/µl |
89.7% |
88.9% |
97.2% |
66.7% |
10.3% |
11.1% |
|
|
PIMA[29–32] |
350 cells/µl |
89.6–100% |
85.7-86.7% |
91.2% |
92.3% |
0-10.4% |
13.3-14.3% |
|
|
500 cells/µl |
95.5% |
84.2% |
76.0-80.2% |
94.9%-96.4 |
4.5% |
15.8% |
We have added sentence: “Also at this threshold, MACS-Countess had PPV and NPV of 96.3% and 85.7%, respectively” (page 11, lines 405-406), and the sentence: “whereas has a lower PPV at 91.2% and a higher NPV at 92.3%” (page 11, lines 408-409).
The sentence: “Positive Predictive Value (PPV) was calculated as the ratio between TP and the sum of TP and FP, Negative Predictive Value (NPV) was the ratio between TN and the sum of TN and FN.” (page 5, line 238 -240).
Point 3: the authors should make an ROC curve to evaluate the sensitivity and specificity.
Answer 3: Thank you for your suggestion. However, creating a ROC curve necessitates a huge number of examined samples, which we do not have at this time. Following your advice, we will increase our sample size in the next phase and generate a ROC curve to assess the sensitivity and specificity.
Author Response File: Author Response.docx
