A Stratified Genomic Framework for Salmonella Serotype Prediction: Evaluation of MLST, SeqSero, SeqSero2, SeqSero2S, and SISTR in Southwest China

Summary

This protocol establishes a stratified genomic workflow for Salmonella serotype prediction, utilizing MLST or SeqSero2S for routine surveillance, SISTR with core genome MLST for outbreak detection, and SeqSero2S for rapid screening. The protocol has been validated through a multicenter analysis to enhance surveillance in China.

Abstract

Accurate serotyping of Salmonella is essential for effective surveillance and outbreak investigation, as serotype diversity directly impacts pathogenicity and public health risk assessment. However, conventional slide agglutination methods are limited by poor reproducibility, labor intensity, and high costs, which hinder their application in high-throughput monitoring programs. To address these limitations, we developed and validated a genomic workflow integrating Multilocus Sequence Typing (MLST), the Salmonella In Silico Typing Resource (SISTR), SeqSero, SeqSero2, and SeqSero2S for serotype prediction using whole-genome sequencing data. This protocol was evaluated through a multicenter analysis of 315 Salmonella isolates collected from food and human sources in Southwest China. The findings of this study demonstrated significantly higher concordance among genomic approaches (up to 100%/99.1%/90.1% in the training set and 100%/97.1%/93.1% in the validation set for SISTR/MLST/SeqSero2S, respectively) compared to traditional serotyping. The workflow includes recommendations for selecting appropriate prediction methods based on surveillance context, emphasizing MLST and SeqSero2S for routine monitoring, SeqSero2S for rapid screening, and SISTR with core genome MLST for outbreak investigations. This approach facilitates the integration of genomic serotyping into public health practice, reducing reliance on traditional serology and improving reproducibility and scalability in Salmonella monitoring programs.

Introduction

Salmonella is among the most significant foodborne pathogens globally, causing substantial morbidity and mortality^1,2. In China, it remains a leading cause of foodborne illnesses, with a limited number of serotypes accounting for the majority of infections³. Accurate serotype identification is crucial for surveillance and outbreak investigations, as serotype diversity is closely correlated with pathogenicity and public health risk. This is well-recognized in common serotypes such as Salmonella enteritidis and Salmonella typhimurium, but some less studied serotypes, such as Salmonella Panama, are also a significant cause of global spread and invasive disease⁴, highlighting the general need for robust serotyping capabilities. Traditional serotyping methods, long recognized as the gold standard, are a time-consuming and high-cost process that requires over 150 antisera⁵. They are further constrained by poor reproducibility⁶, labor-intensive workflows⁷, and inherent variability due to subjective interpretation of agglutination reactions⁸, thus making them inadequate for high-throughput monitoring programs⁹.

The advent of whole-genome sequencing (WGS) has transformed pathogen characterization by enabling rapid, high-resolution serotype prediction^10,11,12. Several in silico tools have been developed, including Multilocus Sequence Typing (MLST), SeqSero¹³, SeqSero2¹⁴, SeqSero2S¹⁵, and the Salmonella In Silico Typing Resource (SISTR)^7,15. While MLST offers standardized, long-term epidemiological tracking¹⁶, SeqSero2 and SeqSero2S allow direct analysis from raw reads and facilitate high-throughput screening of common serotypes^14,15. Conversely, SISTR requires assembled genomes but integrates antigen gene detection with core genome multilocus sequence typing (cgMLST), providing comprehensive resolution for outbreak investigations¹⁷. While WGS-based Salmonella characterization is increasingly adopted as primary evidence in research^16,18,19, its application in large-scale surveillance remains limited, a gap partly attributed to unaddressed practical considerations that influence method suitability. A global survey revealed that only 8% of participating laboratories use WGS for routine surveillance, with analytical complexity being a major barrier²⁰. For instance, reliable serotype prediction hinges on adequate sequencing depth (typically >75× for cost-effectiveness, ideally ~200× for high-quality analysis) and high-quality genome assemblies (N50 of contigs > 30 kbp as a cost-effective minimum, preferably >100 kbp), particularly for assembly-dependent tools like SISTR^21,22,23. Additionally, tool selection must align with a laboratory's computational resources and expertise: open-source tools such as SeqSero2S offer faster alternatives for rapid screening by analyzing raw reads and bypassing computationally intensive assembly¹⁵, while laboratories with limited bioinformatics capacity may struggle with data analysis and storage. Consequently, insufficient data exist to determine whether serological testing can be reduced in large-scale surveillance^20,24, thereby highlighting the need for resource-adaptive approaches. Notably, following the COVID-19 pandemic, there is a substantial amount of idle sequencing instrumentation and qPCR across various levels of healthcare institutions, presenting an opportunity to repurpose this capacity for genomic surveillance.

To address this gap, we conducted a multicenter evaluation of the above-mentioned genomic serotyping tools using 315 Salmonella isolates collected from food and human sources in Southwest China^25,26. Based on these findings, we developed a stratified workflow that accounts for varying laboratory resources and surveillance needs, providing clear criteria for tool selection in different contexts. This framework prioritizes SeqSero2S for rapid screening and SISTR combined with cgMLST for outbreak investigations, thereby facilitating the adoption of genomic approaches and reducing dependence on traditional serotyping methods. Here, we describe each step of the workflow in detail, including isolate identification, sequencing, data analysis, and criteria for selecting prediction tools in different surveillance contexts.

Protocol

The collection and handling of human fecal samples were conducted under approval from the Ethics Committee of West China Fourth Hospital, Sichuan University (Protocol Number: HXSY-EC-2022093), in accordance with the Helsinki Declaration and Good Clinical Practice. Informed consent was waived in accordance with national public health regulations.

NOTE: All procedures involving live Salmonella cultures and human fecal samples must be conducted in a Biosafety Level 2 (BSL-2) laboratory by personnel trained in standard microbiological practices and biosafety procedures. Wear appropriate personal protective equipment (PPE) at all times, including a lab coat, gloves, and safety goggles. Disinfect and change contaminated gloves regularly. Decontaminate all work surfaces before and after experiments with an appropriate disinfectant (e.g., 70% ethanol, 1% sodium hypochlorite). Decontaminate all liquid and solid waste containing biological materials by autoclaving (e.g., at 121 °C for at least 30 min) before disposal. Avoid the generation of aerosols and splashes. Perform all procedures that may create aerosols (e.g., centrifugation, vigorous shaking) inside a certified biological safety cabinet.

1. Food sample collection and strain identification

1. Collect food samples from retail markets or processing facilities. Isolate Salmonella from food samples according to national food safety standard GB 4789.4 and the Foodborne Disease Surveillance Program's confidentiality protocols.
2. Pre-enrichment:
   1. Weigh exactly 25 g of each food sample. Add 225 mL of Buffered Peptone Water (BPW) to the sample (1:10 dilution ratio) and vortex for 30 s to ensure full homogenization and submersion.
   2. Incubate the sample in BPW at 36°C ± 1°C for 8-18 h in a shaking incubator set to 150 rpm.
      NOTE: Pre-enrichment in non-selective broth significantly increases the pathogen load. Handle all subsequent materials (containers, pipettes, etc.) as highly infectious.
3. Selective enrichment: After pre-enrichment, transfer 1 mL of the sample to Tetrathionate Broth (TTB). Incubate the TTB at 42 °C for 18-24 h without shaking.
4. Isolation: After incubation, streak the enriched culture onto the following selective agar plates: Hektoen Enteric (HE) agar, Xylose Lysine Deoxycholate (XLD) agar, and CHROMagar Salmonella. Incubate the plates at 36 °C for 18-24 h.
   NOTE: When streaking plates, be cautious to avoid creating aerosols and cross-contamination. On HE agar, presumptive Salmonella colonies typically appear as blue-green to blue in color, with or without black centers. The surrounding agar may take on a similar greenish hue. On XLD agar, presumptive Salmonella colonies are typically red with black centers. On CHROMagar, presumptive Salmonella colonies are characterized by their magenta to mauve color, as specified by the manufacturer. On each selective plate, expect well-isolated colonies after 18-24 h of incubation. Overgrown plates (>300 colonies) may require dilution and re-streaking.
5. Identification: Examine the plates for suspected Salmonella colonies. Identify suspected strains using an automated identification system with a Gram-negative (GN) identification card, following the manufacturer's instructions for analysis.
   NOTE: Ensure the lot numbers of the GN cards are valid and within the expiration date.
   1. Prepare a bacterial suspension from a pure colony to the specified turbidity (0.50-0.63 McFarland standard) using 0.45% saline solution.
   2. Load the suspension into the GN card and run the test. Ensure the system is set to automatically incubate the card at 35.0 °C ± 2.0 °C and monitor biochemical reactions for up to 8 h.
   3. Accept a result of Salmonella enterica ssp. enterica with a ≥ 95% probability and Excellent confidence rating.
6. Serotyping: Pick single colonies from the Swarm agar plate after overnight incubation at 37 °C. Perform slide agglutination tests to detect O-antigens and H-antigens. Use commercially available Salmonella antiserum kits (see Table of Materials) for testing.

2. Human sample collection and strain identification

NOTE: For the isolation of Salmonella from human fecal samples, follow the Foodborne Disease Surveillance Program's confidentiality protocols.

1. Collection: Collect approximately 2 g of freshly voided human fecal samples. Place the fecal samples in clean, dry, wide-mouth containers. Transport the fecal samples to the laboratory within 1 h using insulated transport boxes to maintain sample integrity.
   CAUTION: Human fecal samples may contain multiple pathogens beyond Salmonella. Handle all samples as potentially infectious and adhere to standard precautions for biohazard material handling.
2. Inoculation: Inoculate the collected fecal samples directly onto the following selective agar plates: CHROMagar Salmonella, Salmonella-XLD agar, and Shigella (SS) agar.
   NOTE: Refer to step 1.4 for the characteristic colony morphology on CHROMagar Salmonella and XLD agar. On SS agar, presumptive Salmonella colonies typically appear as colorless, opaque, or tan with black centers. The agar itself remains relatively unchanged.
3. Identification: Identify suspected Salmonella colonies as described in step 1.5.
4. Serotyping: Perform serotyping by slide agglutination as described in step 1.6.

3. Library preparation and whole-genome sequencing

1. Bacterial culture: Streak Salmonella on LB agar, incubate at 37 °C for 12 h. Pick a colony, inoculate into 200 mL LB broth, and shake at 150 rpm (37 °C, 12 h). Centrifuge at 12,000 × g for 10 min to harvest cells.
   NOTE: Perform all centrifugation steps with sealed rotors or safety cups to prevent aerosol generation.
2. DNA extraction: Extract genomic DNA using a commercial DNA extraction kit (see Table of Materials), following the manufacturer's protocol.
   CAUTION: The reagents in commercial DNA extraction kits (e.g., lysis buffers, proteinase K) may be harmful if inhaled or upon contact with skin and eyes. Perform the extraction in a fume hood or biological safety cabinet if volatile reagents are used, and wear appropriate PPE.
3. DNA quality assessment: Purify genomic DNA and quantify using a fluorometer with the dsDNA High-Sensitivity Assay Kit. Verify DNA integrity by electrophoresis on a 1% agarose gel at 100 V for 45 min; high-quality DNA appears as a single compact band >20 kbp with minimal smearing²⁷.
   NOTE: (Critical step) High-quality, high-molecular-weight genomic DNA (with an A260/A280 ratio of ~1.8-2.0) is crucial for successful library preparation and sequencing.
   CAUTION: The nucleic acid staining dyes used in agarose gel electrophoresis (e.g., GelRed, Ethidium Bromide) are mutagenic. Always wear gloves when handling gels and staining solutions, and dispose of them according to institutional hazardous waste regulations.
4. DNA fragmentation: Fragment approximately 150 ng of qualified genomic DNA to an average size of ~350 bp using a focused-ultrasonication system according to the manufacturer's protocol.
5. Construct the sequencing library from the sheared DNA fragments using a commercial library preparation kit (see Table of Materials). Perform end-repair and 5'-phosphorylation of the sheared DNA fragments. Then, add a single 'A' base to the 3' ends, and ligate the indexed sequencing adapters.
6. Amplify and enrich the adapter-ligated DNA fragments via a limited-cycle PCR program as specified in the kit.
7. Qualify and quantify the final library using methods such as fluorometry and capillary electrophoresis.
8. Perform paired-end sequencing (2 × 150 bp) on a sequencing platform following the standard operational procedures.

4. Data analysis

1. Quality control of raw sequencing data: Perform initial quality control on the raw FASTQ files using fastp (version 0.23.0) with default parameters. The goal is to remove adapter sequences and low-quality reads, thereby generating clean FASTQ data for downstream analysis.
2. Assemble the high-quality, filtered reads into draft genomes using SPAdes (version 3.15.0) with the careful option to reduce mismatches and short indels²⁶.
3. Utilize the genome sequences (FASTQ /FASTA files) as input for the various serotype prediction.
   1. For MLST, import the FASTQ file into BioNumerics software (version 7.6).
      1. In the Import Template window, click Edit parsing under advanced options. Set the Data parsing string and Data decoration to [DATA]. Apply the regular expressions DATA]_* and [DATA] for filename parsing. Save the template, select it, and click Next then Finish. Confirm submission in the Submit Task window to upload data. The CEStoreUploader tool will automatically open to transfer WGS raw data to the calculation engine.
      2. Select the genome entry after upload. Navigate to WGS Tools and select "TraNetGenotype" for automatic ST prediction. In the TraNetGenotype dialog box, select Salmonella as the strain type, select Serotype Analysis, Virulence Analysis, and Antimicrobial Susceptibility Analysis, and click Yes.
   2. For SISTR (version 1.1.3; https://github.com/phac-nml/sistr_cmd; Parameters: all parameters are default), open a terminal and navigate to the directory containing the assembled genome file assembly.fasta.
      1. Run the following command to perform serovar prediction and generate a comprehensive report in CSV format: sistr --qc --alleles-output allele-results.json --cgmlst-profiles cgmlst-profiles.csv -f tab -o sistr-output.tab -i assembly.fasta. This command generates a report containing the predicted serovar.
         NOTE: -qc: Performs quality control checks on the assembly. -i assembly.fasta: Specifies the input FASTA file. -f csv: Defines the output format as CSV. -o sistr_results: Sets the base name for the output file (e.g., sistr_results.csv). -p 4: Uses 4 CPU threads for the analysis to speed up computation. --alleles-output: saves allele sequences and information to a JSON file. --cgmlst-profiles: exports cgMLST allele profiles to a CSV file.
   3. For SeqSero (version 1.0; https://github.com/denglab/SeqSero; Parameters: all parameters are default), navigate to the directory containing the input data file. Run the following command with default parameters for analysis: python SeqSero.py -m 4 -i assembly.fasta. The tool creates an output directory containing the file Seqsero_result.txt with the predicted serotype.
      NOTE: -m 4 specifies input type as genome assembly. -i defines the input FASTA file.
   4. For SeqSero2 (version 1.3.1; https://github.com/denglab/SeqSero2; Parameters: all parameters are default). Run the following command: python3 SeqSero2_package.py -m k -t 4 -i assembly.fasta. The tool outputs the predicted serotype directly to the terminal screen upon completion. The output provides the predicted serotype.
      NOTE: -m k: specifies k-mer-based workflow for rapid serotype prediction using unique k-mers of serotype determinants. -t 4: indicates input type as assembled sequences.
   5. For SeqSero2S (version 1.1.1; https://github.com/denglab/SeqSero2S; Parameters: all parameters are default): Run it using the provided script: python SeqSero2S.py -m k -t 4 -i assembly.fasta. The output provides the predicted serotype.
      NOTE: -m k enables k-mer-based analysis of raw reads or genome assemblies. -t 4 specifies assembled sequence input.

5. Critical parameters and practical considerations

NOTE: The following considerations are crucial for the successful implementation of this genomic workflow, especially in resource-limited settings.

1. Verify that the raw sequencing data achieves a sufficient depth of coverage. A sequencing depth of approximately 100x is typically sufficient for high-quality analysis.
   NOTE: For laboratories prioritizing cost-effectiveness, a minimum sequencing depth of 75x is recommended as a practical lower threshold for reliable serotype prediction using MLST or SeqSero2²¹.
2. Confirm that the quality of the raw reads is high, with a Q30 score (base call accuracy of 99.9%) typically above 90%.
3. Assess the quality of the de novo genome assembly before proceeding with serotype prediction tools that require assembled contigs. Use the N50 statistic as a key metric.
   NOTE: An N50 of at least 100 kbp is recommended²², while an N50 greater than 30 kbp should be regarded as the minimum requirement for reliable analysis²³.
4. Check the completeness of the assembly by ensuring a read mapping rate of at least ≥98% when the filtered reads are mapped back to the assembled genome.

6. Recommended serotype prediction protocol for high-throughput salmonella surveillance

NOTE: Select serotype prediction methods based on surveillance context, required accuracy, and available laboratory resources. For high-throughput Salmonella surveillance, follow the protocol below:

1. Preliminary Identification: Conduct initial screening in accordance with GB 4789.4 to confirm isolates as Salmonella spp.
   NOTE: At this stage, there is no need to identify the specific serotype of the isolates.
2. Routine surveillance (High-Throughput mode): For routine surveillance, since BioNumerics is no longer updated, use open source MLST²⁸, SeqSero2S, or other validated, user-friendly genomic analysis platforms that receive ongoing technical support and updates.
   ​NOTE: For MLST: The 7-gene scheme offers a standardized, portable, and phylogenetically informative profile (Sequence Type, ST) that is ideal for long-term epidemiology and global strain comparison. Its simplicity ensures fast analysis and minimal computational burden. For SeqSero2S: It provides the dual advantage of rapid serotype prediction and integrated MLST results in a single run. It can analyze raw sequencing reads directly, bypassing genome assembly to save time and resources. Even with assembled FASTA data, it is faster than tools like SISTR, as it performs serotype prediction independently without relying on computational analysis.
3. Rapid screening (Time-sensitive settings): For rapid results or in laboratories with limited computational capacity, use SeqSero2S for preliminary serotype prediction.
   NOTE: When performing SeqSero2S predictions, it is basically recommended to interpret "I_4,[5],12:i:-" as Salmonella Typhimurium.
4. Outbreak investigation (High-accuracy mode): For outbreak tracing requiring high resolution, use SISTR, which uses cgMLST to supplement serotype prediction and to provide high-resolution cluster analysis for source tracing.
5. Quality control and inconsistent results confirmation:
   1. For common sequencing type strains, perform random verification. For non-common ones, use a verification ratio of 20%. In cases of inconsistent serotyping results, refer isolates to a higher-level or reference laboratory for a definitive result.
   2. For confirmatory analysis, employ multiple validated methods including: repeated genomic analysis with at least two different in silico prediction tools; repeated in silico prediction with at least two different tools; phylogenetic contextualization using high-resolution WGS-based methods (e.g., cgMLST or whole-genome SNP analysis)²⁹.
   3. Assign the final serotype based on consensus from this comprehensive evidence.
      NOTE: The recommended 20% verification ratio for non-common serotypes is based on the observation that genomic tools maintain high concordance (93.3% for SISTR vs. MLST, 95.0% for SISTR vs. SeqSero2S) even among non-common serotypes (Table 1). This threshold is cost-effective and provides a sufficient safety margin to detect the low frequency of discrepancies among genomic tools in routine surveillance.

7. Statistical analysis

1. Perform data analysis using R software (version 4.1.2).
2. Present categorical variables as frequencies with percentages.
3. Compare groups using χ² tests for categorical data³⁰, consider a P-value <0.05 as statistically significant.

Results

The protocol was validated using 315 Salmonella isolates from Southwest China, with 213 isolates (2/3) serving as the training set to compare five WGS-based serotype prediction methods (MLST, SISTR, SeqSero, SeqSero2, and SeqSero2S) against conventional serotyping results, while the remaining 102 isolates (1/3) were used as a validation set to assess MLST, SISTR, and SeqSero2S performance.

Training set analysis (n = 213)
Reproducibility assessment:

Discussion

This large-scale evaluation, encompassing 315 Salmonella isolates from Southwest China, demonstrates the superior reproducibility of WGS-based serotyping methods compared to conventional approaches (84.1% vs. 62.9% coverage, P<0.001). Our findings validate a comprehensive genomic workflow where critical initial steps of sample preparation and DNA quality control (sections 1-3) directly impact downstream reliability. The protocol emphasizes high-quality genome assemblies (preferably >100 kbp) for assembly...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
BioNumerics (version 7.6)	Applied Maths	https://bionumerics.software.informer.com/7.6/	MLST/serotype prediction/phylogenic analysis
CHROMagar Salmonella	Becton, Dickinson and Company	214983	Selective Salmonella chromogenic medium
Fastp (version 0.23.0)	Open-source	https://github.com/OpenGene/fastp	Read quality control
Illumina NovaSeq 6000 Sequencing System	Illumina	20012850	Whole-genome sequencing platform
M220 Focused-ultrasonicator	Covaris	500295	DNA shearing
Magbeads Fast DNA Kit for Soil	MP Biomedicals	MP116560200	Genomic DNA extraction kit
Microsoft Excel 2016	Microsoft	KB5002794	Data management
NEXTFLEX Rapid DNA-Seq Kit	Revvity	50-255-1124	Sequencing library preparation
Qubit 2.0 Fluorometer	Thermo Fisher Scientific	Q32866	DNA quantification
Qubit dsDNA High-Sensitivity Assay Kit	Thermo Fisher Scientific	Q32851	DNA quantification
R (version 4.1.2)	R Foundation	https://www.r-project.org/	Statistical computing
Salmonella Antiserum Kit	Statens Serum Institute	60898	O/H-antigen detection
SeqSero	Open-source	https://github.com/denglab/SeqSero	Serotype prediction
SeqSero2	Open-source	https://github.com/denglab/SeqSero2	Serotype prediction
SeqSero2S	Open-source	https://github.com/denglab/SeqSero2s	Serotype prediction
SISTR	Open-source	https://github.com/phac-nml/sistr_cmd	Serotype prediction
SPAdes (version 3.15.0)	Open-source	https://github.com/ablab/spades	Genome assembly
Swarm Agar	BD Biosciences	211519	Used for serotyping incubation
TBS-380 Fluorometer	Turner BioSystems	P/N 3800-003	DNA quantification
VITEK 2 Compact System	BioMérieux	95061-768	Automated microbial identification system