Critical evaluation of current isolation, detection, and genotyping methods of Cryptosporidium species and future direction

Abstract

Globally, Cryptosporidium continues to pose a significant health and economic burden despite significant efforts to develop effective on-site biosecurity and best management practices. According to the Global Burden of Disease Study (2016), this parasitic protozoan is responsible for more than 48 thousand deaths in children under the age of five and 7.2 million disability-adjusted life-years worldwide. Additionally, most Cryptosporidium species (e.g., Cryptosporidium hominis, Cryptosporidium parvum, etc.) are resistant to the majority of common disinfectants and can survive outside the body for extended periods. Consequently, it is crucial to establish a prompt and accurate diagnosis as well as the severity of the disease to prevent the spread of parasites and to enable effective management strategies. USEPA Method 1623 is currently used in centralized laboratories to perform routine diagnostic tests for this parasite. This process is laborious, expensive, and time-consuming. In the past ten years, a multitude of techniques based on conventional molecular biology techniques, such as polymerase chain reaction (PCR), nested-PCR, loop-mediated isothermal amplification (LAMP), recombinase polymerase amplification (RPA), and nucleic acid sequence-based amplification, have been utilized to analyze Cryptosporidium species. Similarly, several biosensing techniques have been developed in recent years, including electrochemical biosensors and the CRISPR-Cas system in lateral flow assays. This review discusses the most frequently employed techniques for isolating, quantifying, and genotyping Cryptosporidium species, and their implication on the diagnostic landscape. A subsequent evaluation of the most significant technical and biological challenges and limitations of these techniques is also undertaken.

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Water impact

Cryptosporidium poses a significant health and economic burden despite efforts in diagnostics, biosecurity and best management practices. To ensure safe water access, independent evaluation of existing and emerging technologies for isolating, quantifying, and genotyping Cryptosporidium species is crucial. This review examines these techniques, discussing their impact on diagnostics. Evaluation of technical and biological challenges and limitations is also included.

1. Introduction

Cryptosporidium species is an obligate intracellular intestinal parasite that infects a wide range of living hosts such as mammals, vertebrates, and avian species.¹ Accidental ingestion of Cryptosporidium oocysts causes cryptosporidiosis, characterized by moderate-to-severe diarrhea (MSD) and diarrhea-associated diseases in individuals. Generally, infections are self-limiting in otherwise healthy adults, however, this could be severe, prolonged, and life-threatening for those who are immunocompromised, malnourished, elderly, or under five years of age.^2,3 The global prevalence (∼7.6%) of Cryptosporidium varied in developed and developing countries, 4.3% and 10.4%, respectively.⁴ According to the Global Burden of Diseases (GBD) 2016, cryptosporidiosis was ranked as the fifth leading cause of diarrhea-related mortality in children under 5 with over 48 000 deaths and 7.2 million disability-adjusted life-years (DALYs) worldwide.⁵ Both symptomatic and asymptomatic cryptosporidiosis in early childhood lead to malnutrition, stunted growth, and poor cognitive development.^6,7 A recent multisite study revealed that Cryptosporidium causes severe episodes of diarrhea in the first year of birth.⁸ However, there is no vaccine available⁹ and nitazoxanide is the only FDA-approved drug used for the treatment of cryptosporidiosis which is restricted to patients with ≥1 year of age.¹⁰ This drug is effective in immunocompetent adults and appears to be ineffective for malnourished children.^11–13

Once the infection is established in a host, Cryptosporidium oocysts undergo alternating asexual and sexual reproduction inside the apical surfaces of the host gastrointestinal tract and produce thick-walled oocysts which are shed in feces (Fig. 1).^14,15 Public water supplies such as catchments and watersheds may easily become contaminated either from direct contact with manure from the animal farm or from pathogen-loaded compost that is applied to fields as a fertilizer and subsequently carried away by heavy precipitation.^16,17 The thickened outer layer of the oocyst protects it from environmental stress and remains infectious in the aquatic environment for months.¹⁸ Moreover, conventional water treatment using chemical disinfection (such as chlorination) has proven to be ineffective, therefore, poorly maintained or operational failure of filtration systems in the public water supply is one of the prime concerns for plausible human exposure to this parasite.¹⁹ One of the first recorded, and most significant Cryptosporidium water incidents happened during 1993 in Milwaukee which directly affected approximately 403 000 people and cited drinking water treatment deficiency as the most likely cause of the outbreak.^20,21 Since then waterborne outbreaks have continued to happen despite all the state-of-art technologies and management practices in place. According to a recent report, Cryptosporidium spp. has been the etiological agent for 60.3% (120) and 63% (381) of waterborne parasitic outbreaks that happened during 2004–2010 and 2011–2016, respectively.²² Although the infection dose varies depending on the species and isolates (10 oocysts for C. hominis TU502,²³ 30 for C. parvum IOWA isolate²⁴), it is considerably very low. Considering all these with the resistant nature of oocysts, and fecal-to-oral route of transmission, this organism is marked as a reference for protozoan pathogen for monitoring drinking water safety by the World Health Organisation (WHO).^25,26

Fig. 1 Simplified life cycle of Cryptosporidium spp.

Waterborne outbreaks directly impact the water industry, particularly in terms of managing a safe municipal water supply and the increased medical expenses incurred by government agencies to ensure public health and wellbeing. However, there are no standardized criteria for measuring economic losses in such cases. Corso et al.²⁷ estimated the medical costs and lost productivity to be $96.2 million for the 1993 Milwaukee outbreak, while Chyzheuskaya et al.²⁸ estimated that the 2007 Cryptosporidium hominis outbreak in Ireland cost the public and private sectors approximately $22.44 million. The long-term impact of community outbreaks is much more detrimental than the direct economic losses. Therefore, in addition to promoting good hygiene practices, prioritizing the sensitive, rapid, and on-site detection of this parasite is crucial as a preventive measure for prompt decision-making and mitigating the devastating public health and economic consequences.

Over the years, isolation and detection have been the main focal points in the analysis of Cryptosporidium from water samples. Various filtration methods, such as membrane,^29–31 cartridge,^32–35 ultrafiltration,^36–39 flocculation, and sedimentation,^40–42 have widely been explored for concentrating and isolating Cryptosporidium from diverse water sources. The use of direct continuous flow centrifugation has made the initial isolation and concentration of oocysts rapid and cost-effective.^43,44 These concentration methods suffer from significant material loss or low recoveries, which can ultimately lead to false-negative detections depending on the sensitivity of the downstream detection methods. The utilization of polymeric materials in fabricating engineered micro and nano-filters has resulted in improved filter durability and enhanced effectiveness in removing debris, thereby aiding the recovery of Cryptosporidium during filtration-based isolation processes.^45–47 In terms of detection, apart from immunofluorescence and nested PCR, other amplification techniques, including loop-mediated isothermal amplification,⁴⁸ and nucleic acid sequence-based amplification,^49–52 have been widely employed for on-site viability analysis of the parasite. Nanotechnology-based methods have also been introduced in recent years for Cryptosporidium detection.^53–57 For instance, CRISPR-Cas12a diagnostics,⁵³ nanoparticle-assisted electrochemical ELISA integration,^55,58 aptamer-based electrochemical assays,⁵⁶ and the use of micromesh⁵⁷ in microfluidic-based capturing of Cryptosporidium have contributed to the advancement of sensitive parasite detection.

It is important to note that approximately 95 percent of human cases are caused by two Cryptosporidium spp., namely C. hominis and C. parvum.² However, there are 44 known species and over 120 genotypes of Cryptosporidium spp. that can infect mammals, birds, fishes, reptiles, and amphibians.² Therefore, genotyping plays a pivotal role in surveillance to identify contamination sources and understand the zoonotic potential of the infecting species. In recent years, an open-source software called CryptoGenotyper has been developed and released which allows users to analyze sequencing data to accurately identify the genotypes of Cryptosporidium spp.⁵⁹ Another community-driven bioinformatics resource and database named CryptoDB⁶⁰ (http://CryptoDB.org) has been launched. This database contains manually curated reference genomes, whole genome sequences, and annotations, along with supplemental bioinformatics analysis and data-mining tools.

Several excellent reviews on the biology, pathology, sequencing, biochemical properties, and detection methodologies of Cryptosporidium spp. can be found in the literature. For instance, Bouzid et al.,⁶¹ Guérin et al.,⁶² and Pinto et al.⁶³ reviewed host–parasite interaction, virulence factor for pathogenesis, and Cryptosporidium life cycle. Feng et al.⁶⁴ have focused on the genetic diversity and population structure of Cryptosporidium, while Certad et al.⁶⁵ have extensively discussed the pathogenic mechanism. Ryan et al.⁶⁶ have reviewed the life cycle, genomics, and vaccine research and later produced a more comprehensive and updated review on the topic.⁶⁷ Khan et al.⁶⁸ have extended the scope of their review and covered the molecular epidemiology of the species. In the review by O'Leary et al.,⁶⁹ the topic was broadened to provide a thorough breakdown of the advantages and disadvantages associated with various methods for analyzing Cryptosporidium species. This review was the first attempt to consider the field from a broader perspective, critically evaluating major breakthroughs in tractable C. parvum oocyst diagnostics and indicating tentative steps toward advancing Cryptosporidium research. Bridle et al.⁷⁰ have reviewed the implementation of miniaturized total analysis systems in Cryptosporidium analysis, while Jain et al.¹⁹ and Luka et al.⁷¹ have focused on current and emerging state-of-the-art methods/tools for the analysis of protozoan cysts and oocysts in water samples. Although most of these review papers summarise isolation⁷² and detection^19,69–71 of Cryptosporidium spp. individually or concurrently, no single review paper has discussed the concurrent isolation, detection, and genotyping of Cryptosporidium spp.

In this review, we first focus on the latest advancements in the isolation and detection of Cryptosporidium following USEPA guidelines. We then shift our focus towards the current demonstrations of genotyping and subtyping techniques for Cryptosporidium species. Finally, we highlight the major challenges involved in concentrating, isolating, detecting, and genotyping Cryptosporidium species in water samples. We also discuss the future directions and perspectives needed to achieve on-site isolation, quantitative detection, viability assessment, and genotyping of Cryptosporidium spp.

2. Conventional methods for Cryptosporidium isolation from water sample

Generally, all USEPA methods involve three significant steps: (i) sample concentration through filtration and elution, (ii) selective separation of oocysts from other debris using Percoll sucrose density gradient centrifugation or immunomagnetic separation (IMS), and (iii) fluorescence staining and detection using fluorescence microscopy (see Fig. 2).^73–76 The filtration of water samples can be carried out in laboratory settings or in the field using a portable pump and filter to concentrate Cryptosporidium and remove unwanted substances from the water sample. Over the years, cartridge (capsule) and membrane filters have been widely used. The cartridge filtration approach has gained popularity due to its association with the early USEPA method. Cartridge filters are membrane filters that are commercially available under various brands, such as Filta-Max™ and Filta-Max Xpress by IDEXX,^77,78 Envirochek™ Polyethersulfone and Envirochek™ Polyester HV by Pall,^34,35 Whatman® CrypTest™ Polycarbonate track-etched membrane (a discontinued product) by Whatman,⁷⁹ microporous polypropylene based Micro Wynd II by AMF/Cuno.⁸⁰

Fig. 2 Sampling, process, and time required to execute USEPA 1623 (reprinted with permission from ref. 70).

Apart from these filters, various polymeric materials such as polycarbonate,^81–83 cellulose acetate,^29–31 cellulose nitrate,^31,84 mixed-cellulose ester^77,85,86 have been used to make the membrane of variable pore sizes between 0.45 μm and 5.0 μm to concentrate Cryptosporidium oocysts from the water sample. The choice of filter depends on the recovery efficiency (RE) of the filter, which refers to the proportion of organisms recovered compared to the total number initially added in a seeded positive control.⁸⁷ Multiple factors, including the filter's pore size, water quality (turbidity), and elution process of filtered parasites, influence the RE. Furthermore, the recovery rate can vary across different sampling sites, sampling times, and sample volumes to be analyzed.⁸⁸ For instance, Lee et al.⁸⁹ demonstrated that all four filters (Filtra-Max filter, Envirochek HV™, CrypTest™, and Sigma+™) exhibited higher RE in source water compared to finished water. Higher seeding amounts of Cryptosporidium in 10 L of water led to improved RE for each filter cartridge, indicating the impact of different sample loads on the RE. In another study by Karim et al.,⁷⁹ the Filtra-Max filter showed the lowest RE when employed for surface and drinking waters, while Envirochek HV and CrypTest™ demonstrated higher RE.^79,89 Furthermore, Kim et al.⁹⁰ found that incomplete elution following filtration accounted for 70% of the loss of oocysts. To address this issue, Inoue et al.⁹¹ proposed the use of a specialized eluting filter solution called PET, which contained sodium pyrophosphate (0.02%), Tween 80 (0.01%), and trisodium EDTA (0.03%). They suggested that regardless of the filtration method, PET improved the dispersion of the filtrate and reduced the adhesion of clay particles to the oocysts, resulting in enhanced capture efficiency and target recovery during immunomagnetic separation.⁹¹ Backwashing the filter after filtration is also an effective strategy to achieve better recovery, although this is limited to the filter design of the Envirochek HV™ filter. In essence, while cartridge filtration offers superior RE compared to membrane filtration, it is relatively expensive and impractical for regular monitoring due to the bulky instrumentation involved. On the other hand, membrane filtration reduces costs but may face clogging issues when processing more than 10 liters of water samples.⁸⁸

Ultrafiltration is another mode of membrane filtration that offers satisfactory RE. For instance, dead-end ultrafiltration (RE, 49–87%)^92,93 and hollow-fiber ultrafiltration (RE, 51–65%)^36,93 have demonstrated superior performance compared to Envirochek HV™, serving as a substitute for USEPA 1623 filtration approaches.^{36–39,92,93} Recently few promising nanofabricated filters have been reported with the higher recovery of oocysts, such as microporous polymeric microfilters (RE 95–99%),⁴⁵ high-flux metallic micro/nano-filtration membrane (RE 85%),⁴⁶ Counter-Flow Micro-Refinery (CFMR) systems (RE 81.3%),⁴⁷ and a metallic filter (RE 84·9%) with an ultrasound transducer for eluting oocysts.⁹⁴ Filtration-free concentration method, like continuous flow centrifugation (CFC), has gained attention due to comparative simplicity, single-step operation, rapidity, and relative affordability.^95–97 Zuckerman et al.⁴³ conducted a cross-laboratory validation of portable continuous flow centrifugation (PCFC) of oocysts and compared it with USEPA method 1623. A recovery rate ranging from 35% to 66.8% was reported depending on the water type, matrices, and volume (10 L to 1000 L) of the water processed. In general, average recovery drops for PCFC when tested water volume increases.⁴³ PCFC received USEPA approval due to its ability to centrifuge over 50 liters of water and concentrate oocysts while maintaining the same or higher level of RE compared to capsule filtration (see Table S1† for comparison).⁷⁴ However, recent research has revealed discrepancies between the field-level performance of CFC and other recognized techniques.⁷²

Overall, USEPA method 1623.1 has been validated for detecting Cryptosporidium spp. (and Giardia) in surface water and water from other sources. The detection of Cryptosporidium in these samples poses extreme analytical challenges since it often exists below the detection limit of the method, typically around 0.2 to 1 oocyst per L (when using 10 L samples).⁹⁷ During the pre-concentration of oocysts from large volumes of water (usually ranging from 10 L to 100 L), oocysts can become contaminated with organic debris, particulate matter, and algal cells of the same size and specific gravity as oocysts. Depending on the water source, more than 10⁶ particles between 2.0 and 15 μm have been reported.⁸⁸ Although IMS helps selectively isolate Cryptosporidium oocysts, it can also be compromised by the dissolved iron content in the water source.⁹⁸

Microscopy-based detection in the final step of this method takes advantage of the specificity of monoclonal antibodies. However, currently available antibodies cannot distinguish species differences on the oocyst wall surface.⁹⁹ Autofluorescence from debris and false-positive responses from oocyst-sized particles continue to complicate the detection process, requiring trained professionals to identify oocysts by observing the DAPI-stained nuclei of the oocyst.⁹⁷ The expensive analysis cost (US $700) per sample, lengthy sample preparation time, and bulky instrumentation make it unsuitable for in-field applications.⁷⁰ Consequently, the USEPA method does not truly fulfill the intended purpose of surveillance, as it lacks prompt sample analysis and instead provides information on outbreaks that have already occurred. While increasing the frequency of sample collection and testing does achieve the objective of monitoring the safety of the drinking water supply in developed countries, the ideal option, regardless of a country's economic status, would be an affordable and effective portable concentration technique combined with rapid molecular-based detection.

3. Nucleic acid-based detection methods

Nucleic acid-based identification of Cryptosporidium involves targeting critical genes, such as the small subunit 18S (SSU) rRNA gene, Cryptosporidium outer wall protein (COWP), 70-kDa heat shock protein (hsp 70), thrombospondin-related adhesive protein (TRAP-C2), and actin gene.¹⁰⁰ These genes are highly suitable for identifying Cryptosporidium species due to the presence of numerous interspecific polymorphisms. Among these genes, the 18S rRNA gene stands out due to its multi-copy nature. 20 copies of the target locus of the 18S rRNA whereas 4 copies hsp 70, COWP, TRAPC-2, and actin gene exist in each oocyst.^101,102 Consequently, targeting this gene provides superior performance (see Table S2† for comparison of molecular methods) in molecular detection compared to other genes.¹⁰¹ Nucleic acid-based detection and identification can be categorized (Fig. 3) into the following two, (i) without molecular amplification such as fluorescent in situ hybridization (FISH), (ii) molecular amplification based such as PCR, qPCR, RT-qPCR, LAMP, RPA, NASBA etc. (iii) sequencing such as Sanger and next generation sequencing. Amplification-based methods predominantly utilizing the 18S rRNA gene, offer several advantages. It provides better sensitivity and specificity, the ability to target different genes through multiplexing, and it is comparatively faster than conventional microscopy or immunoassay methods.¹⁰³ Particularly in water sample analysis, oocysts isolated through IMS can undergo DNA extraction either before or after microscopic examination for molecular identification.

Fig. 3 Nucleic acid-based identification methods for Cryptosporidium oocyst detection consist of two main groups which are molecular detection and sequencing techniques.

Fluorescent in situ hybridisation (FISH) was developed by Delong et al.¹⁰⁴ to detect formaldehyde-fixed intact microbial cells. It uses fluorescently labelled oligodeoxynucleotides complementary to 16S ribosomal RNA (rRNA) in situ and visualised (detected) by fluorescent microscopy.¹⁰⁴ FISH has proved to be useful for microbial identification from mixed populations.¹⁰⁵ Vesey et al. is one of the pioneers who designed a FISH probe named CRY1 probe (see Table S1† for primers and probe information), for simultaneous detection and analysis of the viability of C. parvum oocysts. CRY1 probe selectivity was tested against C. parvum, C. baileyi, and C. muris oocysts and found to be specific for C. parvum.¹⁰⁶ 18S rRNA copies (3.5 × 10⁵) per oocyst is the limiting factor for FISH and Deere et al.¹⁰⁷ concluded that fluorescence signal intensities cannot be increased beyond the levels reached by Vesey et al.¹⁰⁶ by optimizing the permeability of oocysts. Following this, they evaluated different fluorochromes for FISH labelling toward flow cytometric detection of C. parvum oocysts. According to them, FITC is the most promising fluorochrome when it is bound to the CRY1 probe with 18-carbon spacer arm with six ethylene glycol moieties which resulted in a fivefold increase in signal intensities compared to FITC directly conjugated to 5′ position of the probe. The exact reason for increased signal intensities was poorly understood and commented that stoichiometry and the physiochemical environment after probe binding could assist in sensitivity.¹⁰⁸ To identify the human infectious Cryptosporidium spp. (C. parvum and C. hominis), Alagappan et al.^109,110 validated two probes (Cpar677 for C. parvum, Chrom253 for C. hominis,). Specificity and cross-reactivity of the probes were evaluated against 8 Cryptosporidium spp. (C. parvum, C. hominis, C. andersoni, C. muris, C. meleagridis, C. felis, C. cervine genotype, and C. rabbit genotype). While Cpar677 did not show any response other than C. parvum, Chrom253 was found to be positive for C. hominis and C. rabbit genotype due to 100 percent 18S rDNA sequence homology between them. These two probes were able to identify more than 80% of C. parvum and C. hominis in a mixture of these two spp. and results agreed with PCR-RFLP of 18S rDNA.^109,110

PCR-based analysis and identification of the pathogen in water originated in the late 1990s.^111–113 Despite the sensitivity, the biggest pitfall for PCR is that it cannot determine the infectivity of the oocyst. Rochelle et al.¹¹⁴ used cell culture and reverse transcriptase PCR of HSP 70 mRNA to assess viability. As mRNA quickly degrades within the cell, it has been established as a reliable marker for testing viability and infectivity. In this assessment, oocysts were inoculated into 70–100 percent confluent monolayer of the Caco-2 cell line. Before extraction of mRNA, cells were incubated at 35 °C for 96 hours. Subsequently, hsp 70 transcript was detected by reverse transcriptase PCR (RT-PCR) with C. parvum-specific primers from extracted mRNA. This approach detected a single oocyte. Later, Di Giovanni et al.¹¹⁵ integrated PCR with in vitro culture (HCT-8 cell line), termed CC-PCR, to test the infectivity of the oocyst by detecting the HSP 70 gene directly from cell lysate without nucleic acid purification. The study was conducted with a total of 122 raw and 121 filter-backwashed water samples, and the results were compared to the immune-fluorescence assay (IFA). While CC-PCR results align with the results of oocyst recoveries from all types of water samples measured by IFA (both IMS-IFA and floatation-IFA), it slightly deviates from the flotation-IFA result when testing environmental samples. CC-PCR detected infectious C. parvum in the 7.4% of total filter-backwash water samples as opposed to 5.8% for flotation-IFA. This discrepancy is unexpected considering flotation-IFA detects all types of oocysts, i.e., live, dead, or infections. Possible reasons for this difference could be the splitting of samples with a small number of oocysts for IFA and CC-PCR, or incomplete dissociation of oocysts in IMS before inoculating cells.¹¹⁵ Although both techniques are comparatively laborious and time-consuming, it has been proposed that CC-PCR, in conjunction with IFA, can be used for routine monitoring of oocysts in drinking water.^116,117

PCR is prone to false-negative responses due to the presence of contaminants in the water sample, such as humic acids, clay, debris, and polyphenols.^118,119 Such outcomes pose a significant health risk as the pathogen may be present but remain undetected. To address this, an internal control, also known as an internal positive control, should be employed to monitor the successful molecular amplification.¹²⁰ This control is a synthetic target distinct from the primary target that is amplified using the same primer set or a different one. Negative amplification of the primary target alone is considered acceptable if the internal control amplifies successfully. To enhance the specificity of the PCR reaction, nested PCR was developed. This technique utilizes two sets of primers to amplify a specific DNA region. The first set of primers binds to the outer region of the target, and the resulting product serves as a template for the second primer pair. By employing nested PCR, any non-specific amplification from the first set of primers will fail to amplify with the second set, thereby increasing the specificity of the reaction. As a result, nested PCR has become a common practice for Cryptosporidium detection in wastewater and catchment water across different regions of the world since the early 2000s.^121–123

By enhancing sensitivity and reducing assay time, quantitative PCR (qPCR) has become an essential tool for environmental pathogen screening.^124–126 One of the key advantages of qPCR is its quantitative nature, providing more than just a presence-or-absence result. Unlike conventional PCR, which mainly analyses the endpoint, qPCR offers a wide dynamic range of detection, enabling the identification of a few copies of the target gene due to its high sensitivity to DNA-binding dyes. Analysis can also be carried out during the assay itself, significantly reducing the total assay time.¹²⁷ Multiplexing capability is another significant benefit of qPCR. For instance, Guy et al.¹²⁴ designed a TaqMan probe-based multiplex assay where simultaneous detection of Cryptosporidium and Giardia spp. In subsequent studies, Hadfield et al.¹⁰⁰ developed a TaqMan probe-based qPCR assay targeting the 18S rRNA gene, which successfully detected all Cryptosporidium spp. regardless of the intraspecies genetic variability in human clinical samples. It is now well known that through proper primer design and melting curve analysis, pathogenic Cryptosporidium species can be differentiated from non-pathogenic ones. Li et al.¹²⁸ demonstrated this by using fluorescence resonance energy transfer probes and melting curve analysis in the 18S-LC1 and hsp 90 assays, allowing the distinction of common human-pathogenic species (C. parvum, C. hominis, and C. meleagridis). Similarly, the 18S-LC2 assay enabled the differentiation of non-pathogenic species (such as C. andersoni) from human-pathogenic species commonly found in the source water. Quantitative detection by qPCR depends on the accuracy of the standard curve, which can be affected by pipetting error and DNA losses. In contrast, droplet digital PCR (ddPCR) provides absolute quantification of DNA/gene copy numbers without the need for a standard curve. Yang et al.¹²⁹ applied and validated ddPCR for the detection of Cryptosporidium spp. (18S rRNA and actin gene) from sheep, cattle, and humans, comparing its performance with qPCR along with a comprehensive cost analysis of these two methods. According to their study, the sensitivity of both methods is comparable. However, ddPCR offers precision at a higher price and is not susceptible to the inhibitors present in feces. Authors suggested calibrating qPCR standards using ddPCR to enhance precision, a method later applied by Zahedi et al.¹³⁰ in analysing Cryptosporidium spp. (18S and gp60 loci) in dam water.

The sensitivity of PCR can be compromised by inhibitors present in the water sample. The loop-mediated isothermal amplification (LAMP) method offers a solution to this challenge. The method utilizes the strand displacement activity of Bst polymerase and detects a target genes within 30–60 minutes at isothermal condition (60–65 °C). This method exhibits unparalleled sensitivity, selectivity, and robustness when it comes to handling environmental samples, surpassing other molecular techniques.^131,132 In the pioneering work by Karanis et al.,⁴⁸ LAMP primers targeting the 60 kDa glycoprotein (gp60) were designed and applied for the detection of Cryptosporidium spp. in fecal and water samples. The researchers achieved an impressive sensitivity that enabled the detection of just 1 oocyst. Subsequent studies have compared LAMP with PCR/nested-PCR, IFA, and other methods for detecting Cryptosporidium in water samples.^133–137 For example, Bakheit et al.¹³³ demonstrated that LAMP could detect Cryptosporidium in samples that tested negative with nested PCR, reaffirming the superiority of LAMP over the more sensitive version of PCR. In their study, LAMP employed primers targeting the SAM-1 and gp60 genes, while nested-PCR targeted the 18S rRNA gene. Mahmoudi et al.¹³⁴ and Gallas-Lindemann et al.¹³⁵ conducted similar investigations, which confirmed the superior performance of LAMP (targeting the SAM-1 gene) in detecting Cryptosporidium when compared to IFA and nested-PCR (targeting the 18S rRNA gene).

Another isothermal amplification process known as nucleic acid sequence-based amplification (NASBA) is used to amplify single-stranded RNA by mimicking retroviral RNA replication by avian myeloblastosis virus reverse transcriptase (AMV RT), RNase H, and T7 RNA polymerase. The RNA serves as a precursor for AMV RT, ensuring the continuous cyclic phase of the reaction at 41 °C for 1.5–2 hours to amplify.^138,139 During RNA amplification by NASBA, potentially contaminating DNA does not interfere amplification condition is set below the melting point of genomic or double-stranded DNA.¹⁴⁰ As it amplifies RNA, it has been used to differentiate live and dead oocysts by amplifying mRNA markers. Most NASBA-based detection and viability testing targeted the hsp70 mRNA.^50–52 Baeumner et al.⁵⁰ were the first to utilize NASBA for detecting viable oocysts in environmental water samples. In their optimized technique, oocysts were isolated using vortex flow filtration and IMS, followed by a 20-minute heat treatment at 45 °C. This heat stimulation induced the expression of the hsp70 gene, which allowed subsequent RNA extraction and NASBA reaction. The authors employed an electrochemiluminescence probe for detecting the amplified products and achieved a limit of detection (LOD) of 10 mRNA molecules (approximately 5 viable oocysts) when IMS was conducted on spiked oocysts in 10 mL of contaminated water. Hønsvall et al.¹⁴¹ used MIC1 mRNA to assess viability and differentiate between C. parvum and C. hominis. The MIC1 gene encodes a protein essential for host cell attachment and invasion, reflecting the infectivity of oocysts. C. parvum possesses two exons in the MIC1 gene, while C. hominis only has the first exon. For the real-time NASBA assay, a specific set of primers and fluorescently labelled probes were designed to target the second exon, while another set targeted the first exon. Differential analysis of the results obtained from both primer amplifications determined whether the sample contained C. parvum or C. hominis. This method detected MIC1 mRNA isolated from as low as 5 oocysts (LOD). mRNA was isolated according to the NucliSens® miniMAG® protocol (Biomérieux, Lyon, France), modified with boiling and proteinase K treatment of oocysts.¹⁴¹ According to a previous report, MIC1 mRNA was detected from both live and inactivated oocysts, which contradicts the reliability of the mRNA-based viability assessment.^{49–52,141,142} The authors argued that RNA remained protected from degradation even after oocyst inactivation.¹⁴¹ RNA analysis continues to be used as a viability marker for Cryptosporidium oocysts, despite reliability concerns. Therefore, more reliable and sensitive methods are needed to expand the options for a comprehensive analysis of Cryptosporidium viability markers.

Until the early 2010s, Sanger sequencing (SgS) was the only tool to be used for specific identification of Cryptosporidium spp. SgS unable to resolve mixed infection or complex DNA mixtures having low-abundance variants without additional molecular cloning methods.^143,144 On the contrary, next-generation sequencing (NGS) has the sensitivity to detect low-abundant DNA copies or amplicons which makes it suitable for epidemiological monitoring. Recently, Braima et al.¹⁴⁵ analyzed cryptosporidiosis cases reported in 2019 and 2020 in Western Australia by SgS and NGS of the gp60 gene. While SgS underestimated the mixed infection, about 28% (5/18) and 5% (4/83) stool samples were reported to be positive for mixed infection by NGS in 2019 and 2020, respectively.¹⁴⁵ Similarly, Bailly et al.¹⁴³ reinvestigated the 102 DNA samples from the Grasse outbreak (French), which happened in 2019. They performed NGS on the gp60 locus and detected a minority subtype (C. parvum-IIaA11G2R1) in a sample, originally undetected by SgS during the initial investigation.¹⁴³ 18S rRNA and gp60 amplicon NGS have been reported for monitoring of Cryptosporidium spp. from river water and riverbed,¹⁴⁶ and wastewater treatment plants.^144,147 A study performed by Zahedi et al.¹⁴⁷ detected no Cryptosporidium spp. with eukaryotic 18S (polymorphic V9 region) NGS but 12/26 were positive with targeted Cryptosporidium-specific NGS, suggesting that the V9 region lacks sensitivity to detect low abundance intestinal species in wastewater. NGS could identify the sample as a mixed infection (false positive) due to polymerase slippage. The use of high-fidelity Taq polymerase, designing primers without degenerate bases, removing chimeric sequences, ensuring strict laboratory practices, and selecting an optimal cutoff value for NGS data processing could reduce the possibility of such identification.^143,145,148

4. Current biosensing methods

In biomedical sciences, point-of-care (POC) devices are used to quickly identify and analyze substances at the patient's location, including within their homes. When it comes to environmental sample analysis, the more appropriate term is “point-of-need” (PON) testing.¹⁴⁹ The advancement of nanotechnology and material science has facilitated the development of efficient, sensitive, and rapid innovative approaches for managing microbial outbreaks. These approaches aim to address the limitations of traditional detection systems and meet some of the ASSURED criteria set by the World Health Organization (WHO): affordability, sensitivity, specificity, user-friendliness, robustness, rapidity, equipment-free operation, and deliverability to those in need.^150,151 In recent years, nanotechnology-based PON platforms, with their rapid, selective, inexpensive, and customizable detection strategies, have gained popularity for water pathogen detection. The following section provides a description of the major sensor types currently utilized for Cryptosporidium oocysts detection. A table (Table S3†) has been provided to understand the strengths and weaknesses of the currently developed sensor.

4.1 Electrochemical detection

In general, the electrochemical biosensor is a type of sensor in which biochemical events, such as antigen–antibody interactions, are transduced into electrical signals. These signals can manifest as changes in current, capacitance, or resistance.¹⁵² In this sensing method, nanomaterials are often used both as tracer tags or host elements that hold recognition components and catalytic moieties.¹⁵³ More recently, direct adsorption of biomolecules onto the electrode surface has also shown great promise in the electrochemical biosensing of various biomolecules.¹⁵⁴ While electrochemical sensors are known for their rapidity, portability, and cost-effectiveness, they often encounter issues with non-specific adsorption. This can result in erratic system behaviour if not handled with the necessary precautions. To address this, measures such as using bovine serum albumin (BSA) to block the exposed electrode surface or purifying the target magnetically before introducing it to the electrode surface are commonly employed.^154,155

When the principle of an electrochemical immunosensor is employed, sensing can be achieved through either label-free methods or enzyme-mediated electron transfer-based approaches. Label-free detection, by definition, does not require any enzyme or nanoparticle-labelled secondary antibody/nanoconjugates. Instead, it depends on the intrinsic properties of the target antigen/organism captured on the primary antibody. For example, in the case of quartz crystal microbalance (QCM), which depends on mass, impedimetric sensors depend on resistance or capacitance.^54,55,156 Luka et al.⁵⁴ presented a label-free capacitive biosensor, as illustrated in Fig. 4(A). They fabricated interdigitated gold electrodes through standard photolithography. Each sensor consisted of 18 pairs of interdigitated electrodes, with a width of 30 μm and length of 500 μm. A self-assembled monolayer (SAM) of recombinant protein G/thiol formed on the gold electrode due to gold-thiol interactions, followed by the modification of anti-Cryptosporidium antibody. The modified electrode successfully captured spiked Cryptosporidium, leading to an increase in relative capacitance proportional to the amount of captured Cryptosporidium oocysts. This sensor exhibited a linear detection range of 15 to 153 oocysts, with a detection limit of 10 oocyst per μL (40 oocysts/5 μL). The same group recently reported an impedimetric sensor based on the previously explored principle of immunosensing.⁴³ This sensor demonstrated increased sensitivity (LOD 4 oocyst per μL), which can be attributed to the larger electrode area of 6.3 mm² compared to 0.27 mm² in the previous report, as well as the careful optimization of experimental parameters.⁵⁵ Although the authors envision that both of these sensors could potentially replace immune-fluorescent measurements performed in USEPA 1623, their performance needs to be reassessed in the presence of an inhibitory matrix of environmental water to establish their efficacy in the field.

Fig. 4 (A) (i) Surface functionalization of interdigitated gold electrodes with mAb and (ii) application for capacitive detection of Cryptosporidium oocysts (reprinted with permission from ref. 54). (B) Schematic representation of an electrochemical apta-sensor for Cryptosporidium detection (reprinted with permission from ref. 56).

The use of nanomaterials as electrode surface modifiers has become a common choice due to their larger surface area, which enhances the binding of recognition molecules on the electrode surface, thereby increasing the chances of binding to the target molecule and amplifying the signal. For instance, Thiruppathiraja et al.⁵⁸ reported a sensitive Cryptosporidium detection method using an electrochemical sandwich ELISA on an Indium tin oxide (ITO) electrode. They functionalized the ITO electrode with (3-aminopropyl) triethoxysilane and AuNPs, enabling surface conductivity and hosting of anti-Cryptosporidium antibodies. Upon recognition of Cryptosporidium on the electrode surface, a dual-labeled AuNP probe (modified with alkaline phosphatase and anti-Cryptosporidium antibodies) formed a sandwich structure. Alkaline phosphatase oxidized p-nitrophenyl phosphate (p-NPP) to p-nitrophenol (p-NP), and the differential pulse voltammetry (DPV) signal was measured.⁵⁸ By comparing this study with Laczka et al.,¹⁵⁷ the impact of nanoparticles on sensitivity becomes evident. Although the two platforms were independent of each other, with the former utilizing AuNP-modified ITO and the latter using only a screen-printed carbon electrode (SPCE) as a transduction element, the high sensitivity of the AuNP-modified ITO electrode (LOD = 3 oocysts per mL) likely resulted from the metallic nanoparticle-assisted surface conductivity and the dual-labeled AuNP probe, in contrast to the SPCE (LOD = 5 × 10² oocysts per mL).

A study conducted by Houssin et al.¹⁵⁶ proposed the use of impedance spectroscopy to analyze the viability of oocysts. According to their findings, a 15% difference in conductance between dead and live oocysts was observed, likely due to metabolic activities. This passive approach aimed to gain insights into the infectivity of the parasite. In a related study, Dibao-Dina et al.¹⁵⁸ investigated the infectivity of Cryptosporidium on HCT-8 cells by measuring impedance. HCT-8 cells were cultivated on an interdigitated microelectrode array (IMA) followed by ensuring infection with sporozoites. Once infection was set, impedance signals were recorded every 7 minutes at frequencies of 1 kHz and 100 kHz. Between 9 and 40 hours after infection, three distinct peaks within the impedance spectra were observed compared to the control (freeze-dried oocysts). These peaks were attributed to the propagation of different stages of the life cycle and the interaction between the host and the parasite, serving as an indicator of infectivity.¹⁵⁸

Due to the high cost and challenges associated with producing and purifying antibodies, DNA/RNA aptamers have emerged as a more affordable alternative in electrochemical biosensing methods. Their ease of synthesis in ubiquitous oligonucleotide facilities makes them highly accessible. Iqbal et al.⁵⁶ developed a library of DNA aptamers and utilized the SELEX (systematic evolution of ligands by exponential enrichment) process to select aptamers with the best affinity for Cryptosporidium. For electrochemical detection, they have immobilised thiolated DNA aptamers onto gold electrodes, allowing interaction with Cryptosporidium. When successful binding occurs between the aptamers and Cryptosporidium, results in change in the conformation of the attached aptamer on the electrode surface. As a result of this electroactive mediator ([Fe(CN)₆]⁴⁻/[Fe(CN)₆]³⁻) can easily be oxidized to the electrode surface, resulting in a change in relative current (Fig. 4(B)). Although this sensor had a detection limit of almost 3 oocysts per μL (100 oocytes/30 μL) and was not as sensitive as reported by Thiruppathiraja et al.⁵⁸ (LOD 3 oocysts per mL), the authors suggested that the sensor holds potential for differentiating between different genotypes, which necessitates careful consideration of aptamer design.⁵⁶

4.2 Optical biosensors

4.2.1 Colorimetric detection. Colorimetric-based detection strategies with color-forming mechanisms are primarily categorized based on the type of analyte being detected. When the analyte consists of the entire oocyst, the concept of ELISA is predominantly employed.^159,160 The colorimetric response typically arises from an enzyme, nanozyme, or nanoparticle-labeled antibody with an appropriate substrate. In the case of detecting analyte-specific nucleic acid sequences, it relies on enzyme-labeled oligonucleotide probes or the specific interaction between nucleic acids and nanoparticles or colorimetric dyes. This type of detection offers the crucial advantages of rapid decision-making through naked-eye observation. They are relatively cost-effective compared to other type of sensors. The enhanced surface area of nanomaterials and their synergistic effects with enzymes or catalytic nanomaterials have significantly facilitated the efficient design of nanoparticle probes for this sensor type. Thiruppathiraja et al.¹⁶⁰ demonstrated this by developing a colorimetric Cryptosporidium immuno-dot blot assay that utilized a dual-labeled AuNPs for antigen recognition and colorimetric signal generation. Initially, AuNPs were modified with C. parvum detection antibody and alkaline phosphatase (ALP) enzyme. Then, naked eye detection of C. parvum was performed using this dual-labeled AuNP probe in a direct ELISA format. This approach exhibited a 500-fold increase in sensitivity compared to conventional ELISA, which employs a secondary antibody modified with ALP, and achieved a detection limit of 10 oocysts per mL.¹⁶⁰ This result aligns with the previously reported electrochemical sensor (LOD = 3 oocysts per mL) using the same procedure.^58,160 However, since Cryptosporidium oocysts are directly adsorbed onto the substrate, the method requires cleaning and purification steps to minimize nonspecific adsorption.

A quartz glass plate was utilized in the sandwich ELISA where the plate was treated with 3-glycidoxypropyl methyl-diethoxysilane (GPTMS) to immobilize the anti-oocyst monoclonal antibody (mAb).¹⁵⁹ Then, the core–shell Ag₂S@silica nanospheres were functionalized with the mAb and employed to recognize the target oocysts captured on the surface of the quartz glass plates. The photoluminescence responses produced by the core–shell Ag₂S@silica nanospheres' inherent properties were measured using UV-visible spectroscopy to quantify the captured oocysts. Both the previously described immuno-dot blot assay and this method exhibited the same level of sensitivity. However, the latter method requires two antibodies and a complex material synthesis protocol, in contrast to the relatively simple nano-conjugation process employed in the dot blot assay process.^159,160

Integrating colorimetric sensors with smartphones enables a direct interface, enhancing user-friendliness and portability of the sensor. Moreover, it facilitates the monitoring of environmental samples in remote locations and the seamless transmission of data to a central database, enabling quick decision-making. Luka et al.¹⁶¹ used this feature of the colorimetric sensors and developed a portable smartphone-based Cryptosporidium RNA detection system (Fig. 5(A)). They used the target-induced color change of AuNPs (gold nanoparticles). Initially, thiolated oligonucleotides were used to modify the AuNPs, which then underwent hybridization with the target in a 3D-printed hydrophobic circular sensing area on a glass slide. Upon recognition of the target by the AuNPs probe, an RNA-linked oligonucleotide network formed, leading to a change in the surface plasmon of the AuNPs. This resulted in a noticeable color change, which was explained elsewhere, specifically for a red-to-blue shift.¹⁶² The sensor demonstrated the capability to detect Cryptosporidium RNA at concentrations as low as 5 μM, without requiring PCR amplification. However, it should be noted that the sensor required purified nucleic acid from a concentrated sample, which limits its suitability for point-of-need (PON) applications.¹⁶¹

Fig. 5 (A) Schematic illustration of label/PCR-free sensing platform by oligonucleotide modified AuNPs (reprinted with permission from ref. 161). (B) CRISPR-based sandwich ELISA (reprinted with permission from ref. 170) and (C) (a) showing the RPA primer binding site with PAM location in GP 60 gene (b) CRISPR-RPA dual mode (fluorescence and colorimetry) dual-mode detection of gp60 gene-based assay (reprinted with permission from ref. 53).

4.2.2 CRISPR-based detection. CRISPR (clustered regularly interspaced short palindromic repeats) and CRISPR-associated (Cas) proteins have garnered significant attention for disease diagnosis and environmental sample monitoring.^163,164 They have been found to cleave nucleic acid in a specific sequence, while certain Cas proteins, such as Cas12a, Cas12b, Cas13a, and Cas14, also exhibit nonspecific trans-cleavage activity.^165–168 Both cis- and trans-cleavage activities have been utilized to detect pathogens, including Cryptosporidium oocysts.^53,169,170 Li et al.¹⁷⁰ developed a sandwich immunoassay based on a 96-well plate ELISA (Fig. 5(B)) to detect C. parvum. The ELISA plate was coated with anti-Cryptosporidium antibodies to capture Cryptosporidium oocysts. Then, an anti-Cryptosporidium antibody conjugated with single-stranded (ss) DNA was introduced to recognize the bound Cryptosporidium. The addition of a CRISPR/Cas12a reaction mixture triggered the activation of Cas12a ribonucleoprotein (RNP) and led to collateral trans-cleavage, releasing the fluorophore from the quencher. Consequently, the platform achieved the detection sensitivity of a single C. parvum oocyst. The authors emphasized that this assay is suitable for detecting Cryptosporidium in diluted mud samples. However, it should be noted that the cost associated with a CRISPR-based system is high despite its high sensitivity.

Very recently, Yu et al.⁵³ reported a lateral flow assay (LFA) device utilizing the Cas12a/crRNA trans-cleavage system, referred to as ReCTC (trans-cleavage). According to their report, a specific region of the gp60 gene was first amplified using recombinase polymerase amplification (RPA), followed by Cas12a/crRNA trans-cleavage. To enable on-site diagnosis of C. parvum, the LFA was based on the cleavage of a specially designed FAM-TTATT-biotin single-stranded DNA (ssDNA) reporter. The FAM-TTATT-biotin ssDNA reporter has the ability to bind specifically to an anti-FITC antibody that is conjugated with AuNPs, forming a complex. In this assay design, if the ssDNA in the reporter remains intact and is not degraded by FnCas12a, the complex can be captured by the biotin ligand fixed at the control line. However, if the ssDNA is degraded, biotin is released from the complex. In this case, the complex without biotin cannot be captured by the biotin ligand at the control line. Instead, it can be captured by the IgG antibody at the test line. During the assay, the ReCTC reaction system is incubated for 1 hour. After that, it is mixed with ddH₂O and loaded onto the LFS pad. The mixture is then incubated at room temperature for a short period of time. Finally, the LFA strips are photographed using a smartphone camera. This method has the capability to detect even a single copy of the cloned C. parvum gp60 gene without the need for tedious extraction of genomic DNA. Importantly, it exhibits no cross-reactivity with other subtypes of C. parvum or other common enteric parasitic protozoa.⁵³

5. On-chip/microfluidics integrated sensor

Microfluidic devices offer the advantage of integrating multiple components, allowing for the occurrence of multiple chemical processes within a single device. This system integration is particularly beneficial for sample isolation, reducing errors caused by human contact and potential contamination, while also improving operational safety. By centralizing all processes within a single device, the time required for sample processing can be significantly reduced, enabling rapid analysis. It has to be considered that the application of microfluidics application to environmental samples often fails due to clogging from suspended colloidal particles or unwanted microbes of bigger sizes than the intended ones.¹⁷¹ Besides, challenges arise when processing large sample volumes (>10 L) to efficiently identify Cryptosporidium oocysts using microfluidic devices, as these devices typically handle sample volumes ranging from μL to mL. Therefore, a pre-enrichment step becomes necessary before loading the sample into the main device. Alternatively, direct sample loading can be achieved if the devices are modified with a filtration system or designed for post-filtrate material analysis. An alternative and improved method to the IMS coupled direct immunofluorescence assay (DFA-IMS) was reported by Taguchi et al.⁵⁷ They utilized cone-shaped microcavities (2 μm and 10 μm diameters on the top and bottom surfaces, respectively) on a special-use stainless 304 (SUS) wafer known as SUS micromesh. When a sample is injected into the microfluidic channel, oocysts are trapped in the micromesh due to the negative pressure applied by adjusting the vacuum at the bottom of the micromesh. Each cavity in the micromesh accommodates only a single oocyst, ensuring aggregation-free conditions and assisting in parasite quantification. This system minimizes non-specific adsorption through the continuous flow of simple antibody solutions, outperforming DFA-IMS by recovering 93.1% of Cryptosporidium oocysts from tap water compared to DFA-IMS's 90.5%. The system offers a shorter detection time (1 hour) compared to DFA-IMS (2.5 hours) and simplified steps, making it an attractive alternative.⁵⁷

Huang et al.^172,173 reported a label-free method with microfluidic multi-angle laser scattering, which is relatively rapid (approximately 30 minutes). Efforts have also been made to sort and pre-concentrate Cryptosporidium using electrohydrodynamic concentration,^174,175 dielectrophoresis-mediated contact-less separation of Cryptosporidium oocysts,^176–178 disposable microfluidic micromixers¹⁷⁹ and hydrodynamic trapping-based microscopy.^178,180 Each method has its advantages: micromixers showed capture efficiency up to 96%, exceeding that of IMS,¹⁶² hydrodynamic trappings demonstrated promise in art-effect free imaging,¹⁷⁹ hydrodynamic trappings showed promise in art-effect free imaging,¹⁸⁰ and dielectrophoretic separation can differentiate between oocysts.^176–178 However, precise fluid control is necessary for optimal functioning, suggesting that these methods are unlikely to be suitable for PON testing. The electrohydrodynamic and dielectrophoretic systems require perfectly buffered conditions to create a dipole within Cryptosporidium, which is likely to be affected by ions from filter-eluted samples.

Instead of detecting the whole Cryptosporidium oocyst, Angus et al.¹⁸¹ focused on detecting COWP (Cryptosporidium oocyst wall protein) using a microfluidic chip coupled with a miniature spectrophotometer. This system utilizes Mie light scattering to detect the immuno-agglutination of COWP protein to antibody-conjugated beads. However, the size of the oocyst (4–5 μm) is not ideal for this type of light scattering. To overcome this, the sample (2 mL) was sonicated to release the target protein, such as COWP. Subsequently, 15 μL of the sonicated sample and antibody-conjugated beads were introduced into two inlets, and a syringe pump created negative pressure, drawing them into the microfluidic channel where immuno-agglutination occurred. The limit of detection for this assay was 10 oocysts per mL of water. Considering the small amount of sample used (15 μL from a 2 mL sample) for analysis, this sensitivity corresponds to detecting sub-single-oocyst levels per sample. The authors envisioned that with proper filtration, this method could potentially detect ≤1 oocyst in a large volume of water sample (e.g., 10 L), which is comparable to the EPA method.^36,181

Microfluidic-based nanosensors have garnered attention for their ability to isolate and detect target nucleic acids on-chip. Nucleic acid sequence-based amplification (NASBA) has proven to be a powerful tool for on-chip analysis of Cryptosporidium mRNA.^138,182,183 In a recent study, Reinholt et al.¹⁸² developed a microfluidic system where mRNA isolation and NASBA-based amplification were conducted in a straight channel (Fig. 6(B)). They explored different surface chemistries and found that polyamidoamine dendrimers provided the best performance for both isolation and in situ NASBA reaction. To detect the amplified NASBA, a Lateral Flow Assay was employed. The NASBA product was hybridized with a reporter probe attached to sulforhodamine B-encapsulating liposomes, and the resulting solution flowed vertically to form a sandwich with C. parvum capture probes, generating a colorimetric signal. This system successfully isolated and amplified mRNA from 30 C. parvum oocysts. This sensitivity is comparable to the sensitivity of the NASBA reaction performed in standard laboratory benchtop devices for molecular amplification. The high immobilization efficiency of the thymidine oligonucleotide oligo (dT)₂₅ on the PAMAM dendrimers' surface contributed to the device's sensitivity by enabling maximum mRNA capture. However, this device relies on a separate detection system for NASBA products.^182,184 In a previous study, Nugen et al.¹⁸³ fabricated a polymethyl methacrylate (PMMA) fluidic device with an integrated electrochemical module containing interdigitated ultramicroelectrode arrays (IDUA) (Fig. 6(C)). NASBA-based amplification of hsp 70 mRNA was performed outside of the PMMA chip, and the product was mixed with microbeads, potassium ferri/ferrocyanide-encapsulated liposomes, and a hybridization solution. The mixture was then pumped in a reciprocating motion through the PMMA chip for efficient mixing and hybridization on magnetic beads. Finally, the magnetic beads were accumulated on the IDUA by placing an external magnet, followed by the lysis of the liposomes with a detergent to release the electroactive substances. Unlike the previous method, the detection step was performed on-chip.^182,183 Both of these methods do not constitute fully integrated systems since the processing and detection steps were conducted externally to the chip. However, by performing specific tasks outside the chip, these systems offer simplicity and efficiency while remaining within the scope of the designed fluidic chip.

Fig. 6 (A) (a) Special use stainless 304 (SUS) micromesh, (b) cross-sectional view of cone-shaped cavity of micromesh, and (c) microfluidic SUS micromesh assembly for Cryptosporidium trapping (redrawn and reprinted with permission from ref. 57). (B) Schematic of mRNA isolation in a straight microfluidic channel ((a) Cryptosporidium lysate, (b) PAMAM dendrimer for surface modification of microfluidic channel, (c) isolated mRNA in dendrimer surface) for (d) in situ NASBA reaction (reprinted with permission from ref. 182), and (C) (a) SEM of IDUA and PDMS channel, (b) a PMMA sheet containing a hot-embossed channel was then bonded to the PMMA containing the IDUA, and (c) assembled chip containing two channels (reprinted with permission from ref. 183).

6. Genotyping and subtyping of Cryptosporidium spp.

Genotyping is a molecular approach used to differentiate samples at the species level, such as distinguishing between C. hominis and C. parvum by analyzing DNA sequence variations in specific genes. This approach is distinct from subtyping, which focuses on variations within a species. While several molecular tools have been developed, there is currently no standardized procedure.¹⁸⁵ Generally, the 18S rRNA and gp60 gene are extensively used as the optimal markers for speciation and subtyping, respectively.⁶⁸ Complete 18S rRNA gene is around 1620–1746 bp (AY954884.1, AY954885.1) long. Within this gene, three domains exhibit significant polymorphism, and one of these domains is key to distinguishing various Cryptosporidium species. Specifically, the discriminating domain lies between nucleotide positions 479–928 (with reference to C. felis AF108862).¹⁸⁶ Nested PCR is employed to amplify this region, generating amplicons of approximately 830 base pairs in size. Following amplification of the ∼830-bp segment of the 18S rRNA gene through nested PCR, the resulting product is digested with two restriction enzymes (e.g., SspI and VspI). The resulting restriction pattern is then compared using gel electrophoresis, as described by Xiao et al., to determine whether it corresponds to C. parvum or C. hominis.^187,188 In cases where the observed restriction pattern does not match any previously reported patterns, the amplicons are sequenced and compared to reference sequences to identify the genotype.¹⁸⁹ Nested PCR is highly sensitive and capable of amplifying DNA from a single purified oocyst, enabling the detection of different species by analyzing PCR product fragments.¹⁹⁰ However, PCR amplification tends to favor the most abundant species, making it challenging to identify rare genotypes in cases of mixed infections. In such instances, deep sequencing of samples can reveal the presence of relatively rare genotypes within the mixed infections.

Historically, PCR-RFLP and more recently, qPCR with high resolution melting curve, Sanger sequencing of qPCR products have been extensively used for species identification and genotyping Cryptosporidium spp. from various sample types, including raw water, drinking water, stool, soil, vegetables, and more.^191–194 Besides 18S rRNAgene, COWP and hsp70, have been explored for genotyping purposes. However, the multi-copy nature and semi-conserved/hypervariable characteristics of the 18S rRNA gene make it ideal for designing genus-specific primers.^188,195,196 It is important to note that the 18S rRNA gene-based genotyping method cannot differentiate intra-species variations. In order to address this limitation, Strong et al.¹⁹⁷ introduced gp60 (also known as gp40/15) gene-based subtyping, which offers high genetic resolution for efficient characterization of C. parvum and C. hominis. This gene is now commonly used to analyze transmission dynamics, including sources of infection, genetic diversity, and host adaptation.¹⁹⁸ The gp60 gene contains a variable number of tandem repeats of serine coding trinucleotides TCA, TCG, or TCT at the 5′ end, which determines the subtypes. At the 3′ end, there is a repetitive sequence and a conserved region (Fig. 7) that distinguishes between C. hominis and C. parvum. According to the gp60 nomenclature, the subtype is indicated by a combination of the subtype family designation (Ia, Ib, Id, Ie, If for C. hominis, and IIa, IIb, IIc, IId for C. parvum) followed by the number of trinucleotide repeats (represented as A, G, or T for TCA, TCG, or TCT, respectively) in the gp60 gene. For example, IbA10G2 represents C. hominis with ten copies of TCA repeats and two copies of TCG repeats in the gp60 gene. Certain subtypes of C. parvum and C. hominis contain additional repetitive sequences, represented as ‘R’, after the trinucleotide repeats. For instance, IaA23R4 indicates a C. hominis subtype with four ‘AA/GGACGGTGGTAAGG’ repeats, while IIaA15G2R2 belongs to C. parvum with two ‘ACATCA’ repeats.^{64,195,199,200} The gp60 subtyping method has proven useful for allelic discrimination and tracing back to sources and transmission routes. It is applicable to the commonly found and closely related species of C. parvum and C. hominis.^199,201 The recombination between different stages of the life cycle contributes to heterogeneity, as seen in the intragenic linkage disequilibrium of natural C. parvum populations.^202,203 More recently, Huang et al.²⁰⁴ reported a complex population structure of C. hominis which is largely inconsistence with gp60 subtypes and supports the fact that gp60 is the region of high recombination. Thus raised a concern of using gp60 as a population marker.²⁰⁵ Environmental samples appear to harbor Cryptosporidium spp. other than C. parvum and C. hominis such as C. felis, C. cuniculus, C. meleagridis, C. canis, C. ubiquitum, which have the potential to infect human.²⁰⁶ As mentioned earlier, gp60 is limited to C. parvum and C. hominis, environmental samples need alternative genotyping methods. Efforts were made to establish gp60-based genotyping for C. felis. However, it turned out to be unsuccessful and suggested currently available low-resolution markers (18S rRNA, COWP and HSP70) for genotyping.²⁰⁷ Therefore, to gain more detailed and discriminatory information about the subtypes multilocus sequence typing (MLST) or multilocus genotyping (MLG) proved to be suitable.¹⁹⁸

Fig. 7 Derivation of gp60 nomenclature for C. hominis and C. parvum (modified and redrawn with permission from ref. 199).

Researchers have studied various loci that primarily contain microsatellites and minisatellite repeats, also known as variable-number tandem repeats (VNTR), for MLG. Fragment sizing or sequence analysis has been employed for this purpose. A systematic review has already been published, which reported a combination of 55 VNTR loci, although the rationale for selecting these loci was not explained.¹⁸⁵ In a study by Nadon et al.²⁰⁸ criteria were defined for selecting loci for multilocus VNTR analysis. According to their report, the loci should have 100% conserved sequences as flanking regions, be distributed across the chromosome as extensively as possible, consist of perfect and homogeneous repeats of at least 5 base pairs, and should not exhibit any apparent insertions or deletions in the repeat unit.²⁰⁸ Pérez-Cordón et al.²⁰⁹ identified 28 loci from whole-genome sequences of C. parvum that met these criteria and were deemed suitable for in vitro evaluation. Recently, Robinson et al.¹⁹⁸ re-evaluated these VNTR loci and proposed an improved set of loci. Compared to the currently used gp60-based genotyping method (which has a discriminatory power of 0.74), the newly proposed VNTR loci exhibited a superior discriminatory power of 0.99.

Besides monitoring the Cryptosporidium spp., whole genome or target amplicon NGS is crucial for identifying new markers for detecting and analyzing the diversity of this species. As mentioned earlier, NGS can resolve each species present in a DNA extract, it helps in finding the intra-host genetic diversity of gp60 subtypes. Grinberg et al.²¹⁰ observed ten gp60 allelic subtypes by NGS in two Cryptosporidium-positive fecal samples in New Zealand, previously thought infections were due to a rare gp60 subtype (C. parvum IIaA21G4R1) during an outbreak in 2006. Similarly, Zahedi et al.¹⁴⁸ also observed gp60 diversity in C. parvum in cattle and rabbits from Australia and China, but only a single subtype (IbA10G2) of C. hominis in Australian Kangaroo. Although subtype population structure is subjected to vary depending on the hosts and region, this observation indicates population structures of C. parvum and C. hominis are panmictic and clonal in gp60 allege respectively circulating in that region. To improve the genotyping resolution of C. hominis IbA10G2, Beser et al.²¹¹ analyzed 17 C. hominis (including nine C. hominis IbA10G2 isolate) whole genome sequencing data and identified 10 single nucleotide variation (SNV) in nine loci of sizes 268–349, spanning in 6 chromosomes. This typing panel has been validated against one-step PCR-based amplicon sequencing of 44 clinical IbA10G2 samples and three publicly available whole genome sequences. By setting a criterion of minimum 20× coverage of each locus, this study was able to group all the IbA10G2 into 10 different sequence types (named ST1–10). Despite having moderate sample sizes from Sweden, Spain, the USA, the UK, Peru, the UAE, Venezuela, Thailand, Tunisia, and Estonia, the true potential of such a marker panel can only be realized if samples from each continent including low- or middle-income countries were included.

7. Discussions and current challenges

Despite continuous efforts spanning over two decades to improve and advance the successful isolation of oocysts from water, there are still persistent challenges that need to be addressed.

(i) Extremely low abundance and recovery

The current gold standard detection approach, USEPA 1623.1, falls short of achieving 100% recovery, meaning that it cannot detect all the pathogens present in the collected water sample. Incomplete elusion of oocysts during the concentration stage often leads to a recovery rate of less than 15%, while in the later stage of IMS-FA, a recovery rate of over 95% is commonly achieved.^212,213 The actual recovery rate varies depending on water quality and turbidity, but USEPA 1623.1 considers a 38% recovery rate acceptable. Depending on the sample to be analysed, and the actual concentration of the oocysts in a sample, this recovery will allow to detect or even quantify the oocysts. Given that high-quality nucleic acid preparation is available from a low number of oocysts, down to single oocysts can be detected (LAMP⁴⁸) and quantified (ddPCR, nested-PCR).¹²⁹ It is evident that achieving a high recovery rate during the concentration stage is crucial for successfully detecting oocysts in the later stage. It should be noted that the concentration of Cryptosporidium in surface water is typically low, usually around 0.1/L or 1 in 10 L. For example, if the oocyst concentration at a sampling point in a stream is 1 in 10 L, many 10 L volumes passing that point would not contain a single organism, while some may contain more than one. This variability greatly impacts the reproducibility of the analysis. In the worst-case scenario, it may result in false-negative identifications, increasing the relative risk of a potential outbreak through human exposure to the pathogen. While increasing the volume of water used for sampling could potentially address this issue, it is impractical as it often leads to filter clogging, depending on the water quality. Use of engineered polymer materials to prevent adhesion of oocyst to the filter/membrane,²¹⁴ efficient design of filtration such as incorporating ultrasound source in filter⁹⁴ may increase the oocyst recovery is the by eluting more trapped oocysts. Indeed, this cannot solve the issue of filter clogging when more water needs to be filtered. In this case, a passive sampling procedure with molecular detection might be suitable which has been reported for SARS-CoV-2 in wastewater surveillance.²¹⁵

(ii) Cryptosporidium viability

In general, Cryptosporidium oocysts can remain viable and infectious for months under environmental conditions and more concerning during drinking and recreational water treatment processes. However, empty oocysts had been reported in varying degrees in environmental samples.¹⁰⁷ If the sample is not analysed quickly after receiving, depending on the storage condition and length, sporozoites may be released from the oocysts.¹⁰⁷ Such oocysts will generate a positive fluorescent signal. Vital dye staining or further, CC-PCR based infectivity test will solve this issue to some extent. Each of these has limitations, such as the study showed that fresh oocysts from feces were importable to vital dye²¹⁶ and CC-PCR is not suitable to get a result within a day. To address this issue, the implementation of automatic image analysis for detection and viability testing holds promise. Not only could it help solve the problem, but it could also reduce the time-consuming nature of the analysis.²¹⁷

(iii) Insufficient available biomarkers

The anti-Cryptosporidium monoclonal antibody combined with IMS is currently used as a routine method for isolating Cryptosporidium from water samples.^{37,74,79,97,117} However, using antibodies for isolation and detection does not allow for differentiation of the genus or subspecies of Cryptosporidium. As discussed earlier, previous studies have shown that C. parvum isolates from various sources, such as humans, calves, pigs, dogs, mice, ferrets, marsupials, and a monkey, exhibit significant genetic diversity, and the distribution of different genotypes in surface water can vary. While the 18S rRNA marker is useful for species identification, it can also be utilized, along with additional markers, for identifying subtypes primarily within C. parvum and C. hominis.^218–220 Certain markers are also limited to analyzing local geographic diversity, and the global population structure may vary.²¹⁸ Organoid model for infection studies,²²¹ hollow fibre technology for continuous culture,²²² single oocyst sequencing²²³ may provide useful information regarding post-meiotic precise genetic characteristics. Single-cell genomics for Cryptosporidium is quite a new and growing branch and ensuring a high-quality reference genome of each species will decipher new ways to understand this species.

(iv) Variability of molecular characteristics (specificity)

In addition to the scarcity of available markers across species, genetic variability has been observed in the sexual life of Cryptosporidium due to recombination.²²⁴ This variability, including SNPs, insertions, or deletions within the markers, could potentially cause the multi-locus typing approach to fail in certain cases. Therefore, it is crucial to sequence all 8 chromosomes of Cryptosporidium spp. directly collected from water samples worldwide and develop location-specific markers for multi-locus typing. However, establishing a clonal population from a single sporozoite may help to understand the virulence and transmissibility of each subtype.²²⁵

8. Conclusion and future direction

In this review, we have discussed the economic impact of Cryptosporidium outbreaks from a public health perspective and provided a comprehensive overview of both conventional and state-of-the-art detection strategies for this parasite. Currently, the USEPA 1623.1 method is considered the gold standard protocol for Cryptosporidium spp. detection and is an integral part of regulated quality control to minimize risks and potential Cryptosporidium outbreaks. However, there is an urgent need for alternative methods that can offer a faster, cheaper, and simpler platform for the isolation, detection, and genotyping of Cryptosporidium, addressing the limitations of the USEPA 1623.1 method. To improve the performance of isolation and detection, one approach is to incorporate filtration and elution in a single device with automated operation and control, which would simplify sample processing, minimize steps, and reduce overall time. However, many of these proof-of-concept studies need further development and validation to achieve a high level of consistency, sensitivity, and specificity to be considered by water authorities and regulators. Another potential advancement is the use of magnetic micro- and nanoparticles directly in the water sample to capture Cryptosporidium oocysts before filtration, presenting a next-generation challenge for a rapid and integrated detection system. Achieving proper sample circulation, effective mixing of magnetic particles, and efficient recovery of oocysts pose bio-engineering challenges. To achieve selective capture and detection of the whole pathogen, monoclonal antibodies are essential. As mentioned earlier, unless molecular signatures are tested, monoclonal antibodies cannot differentiate between species. DNA/RNA aptamers could offer hope in such cases. Aptamers have gained attention as alternative biorecognition elements,^56,226–228 but systematic screening of aptamers specific to different species/genotypes of Cryptosporidium is currently lacking.

Recent efforts have indicated that the utilization of nanoparticle-coupled nucleic-acid-based detection techniques holds potential for enhancing the sensitivity of oocyst isolation and detection. An example of such a technique is the colorimetric assay developed by Luka et al.¹⁶¹ which utilizes the aggregation of AuNPs that are linked to specific oligonucleotides to capture Cryptosporidium RNA. This approach presents a simple, label-free, and PCR-free method of detection. It is however crucial to carefully control nonspecific adsorption to ensure accurate and reliable results. In addition to nanoparticle-coupled nucleic-acid-based techniques, implementing artificial intelligence (AI) and machine learning-based automatic image processing can have a transformative impact on parasite detection. By minimizing artifacts and improving the accuracy of miniaturized systems, these technologies can revolutionize the field. They have the potential to enhance the efficiency and precision of detecting parasites, providing valuable insights for diagnosis and research.

Genotyping and subtyping of Cryptosporidium present significant challenges that need to be addressed. This is particularly important in developing new sensors for Cryptosporidium since species are host-specific, and in most cases, only two species are infectious to humans. The difficulty lies in the fact that Cryptosporidium spp. share an almost identical genome, making sequencing the only practical solution for analysis. However, the cost and time required for sequencing pose limitations. Despite this, multi-locus genotyping, along with gp60-based genotyping, has shown partial success in speciating isolates and tracing oocysts back to their host/source. Currently, the understanding of cryptosporidiosis epidemiology is fragmented due to the lack of genotype comparisons across wide geographical areas and inconsistencies in recording diagnostic and epidemiological data. Informative genotype comparisons and meta-analyses encompassing multiple surveys could potentially establish a standard genotyping scheme and enhance our understanding of cryptosporidiosis epidemiology.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

The authors acknowledge the higher degree research scholarships, GUIPRS and GUPRS Scholarships, to R. G. M. from Griffith University.

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Footnote

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d3ew00469d

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