Table 2.

Advantages and disadvantages of the main computational methodologies available to quantify NETs in vitro and in situ.

NET staining technique	Compatible quantification method	Advantages	Disadvantages	Selected references
SYTOX	DANA	Easy to follow tutorials, individual cell analysis, exclusion of false positives, high reproducibility and robustness, reduced analysis time	Human optimisation required, confirmation with additional NET markers required	(23, 45)
	3D-CSLM	Highly sensitive, robust	Skilled 3D-CSLM operator required, false positives, confirmation with additional NET markers required	(46)
	Plate assay	Fully automated, high-throughput, robust	False positives, non-visualization of NETs, confirmation with additional NET markers required	(24, 30)
IFM	ImageJ	Use of freeware, robust	Possible reproducibility problems across laboratories, possible sampling bias, difficult to implement, human input required, clumping cells quantified as one	(37)
	NETQUANT	Fully automated, easy to implement, reproducible and robust, individual cell analysis with multiple NET criteria, exclusion of false positives, high-throughput, advanced post-analysis data	MATLAB licence required	(25)
	Machine learning	Fully automated, high-throughput, sensitive, reproducible, exclusion of false positives	Informatics knowledge required, training for new conditions required, clumping cells quantified as one	(22, 47)
MIFC	Machine learning	Fully automated, high-throughput, sensitive, reproducible, exclusion of false positives	Informatics knowledge required, training for new conditions required	(46)
In situ sections	Machine learning	Fully automated, high-throughput, sensitive, reproducible, exclusion of false positives	Informatics knowledge required, training for new conditions required	(48)
	CSLM	Specific, easier to implement than machine learning protocols	Specific software required	(49)
	ImageJ	Use of freeware, robust	Additional NET markers required, subject to false positives	(50)