Contamination frequently compromises the accuracy and integrity of research in today’s laboratories. When biological, chemical, or physical impurities infiltrate a laboratory environment, samples, disposables or operating surfaces, false-positive or false-negative results can be generated. This can invalidate research and require redundant testing. Furthermore, when the contamination source remains elusive, it places an immense financial burden on the facility for deep-cleaning and replacing reagents. When dealing with high-precision methodologies (e. g., sequencing or PCR), even the most negligible amount of contamination becomes a critical factor.

A significant challenge is rooted in cross-contamination between specimens and zones, and occasionally even across facilities handling identical samples [1]. Consequently, establishing a unified diagnostic database for contamination will prove profoundly beneficial in reducing financial costs. Once the source of the contamination has been detected, its complete mitigation becomes vastly more manageable.

TYPES OF CONTAMINATION OF CELL LINES

Cell line contamination is broadly classified into chemical and biological origins. Chemical issues stem from media, sera or water impurities and endotoxins, whereas biological threats involve various microorganisms such as bacteria, fungi, yeast, viruses, mycoplasma and cross-contamination from other cell lines. The widespread accidental mixing of HeLa and other highly proliferative cell lines into various cultures is a well-recognized issue that has severe repercussions. Yeast and mold belong to potential contaminants. Their detection is possible by visual inspection and/or microbiological inoculation [2].

Mycoplasma contamination of cell cultures is a widespread phenomenon that causes the main pathological effects in cellular physiology and metabolism. Unlike typical bacterial contaminants, mycoplasmas are exceedingly difficult to identify via light microscopy due to their microscopic dimensions, slow proliferation, and the insidious pathogenesis of their infections — characterized notably by the absence of direct eukaryotic cytotoxicity. Contamination is primarily identified by the structural degradation of the culture, along with noticeable shifts in cellular physiology and behavioral patterns [3, 4]. To identify mycoplasmas, researchers rely on a multifaceted approach utilizing polymerase chain reaction (PCR), immunostaining, autoradiography, microbiological analysis, and electron microscopy. Despite the recent development of various fluorescent dyes designed to refine mycoplasma identification, the critical balance between diagnostic speed and financial viability remains unresolved [5, 6].

Antibiotics (macrolides, quinolones) are used to treat mycoplasmosis. They are selectively administered based on pathogen vulnerability. Once Mycoplasma genitalium is identified as an obligate pathogen, antimicrobial treatment is administered. For conditionally pathogenic mycoplasmas, diagnostic relevance demands the pathogen be identified at a concentration of 10⁴ microbial cells per ml of specimen [7].

Detecting viral contamination is a challenge, primarily because these submicroscopic pathogens act as intracellular parasites that exploit host cellular systems to multiply. Furthermore, their microscopic proportion makes it difficult to isolate and eradicate them from laboratory reagents. Working with virus-infected cell lines constitutes a critical biohazard for research integrity and laboratory staff. This danger is particularly acute when the host matrices are derived from human or primate tissues [1, 2].

Unlike mycoplasma — which can contaminate any cell line — viral infections are highly specific and depend entirely on the particular cell line being used. For instance, mouse hepatitis virus only infects specific mouse cell lines like Neuro-2a and RAW 264.7, while Theiler’s virus specifically targets mouse neuroblastoma cells (Neuro-2a) [8–10]. However, viruses frequently infect most lab cell cultures. In 2024, researchers evaluated a virus known as BVDV (Bovine Viral Diarrhea Virus), noting that its introduction into a laboratory setting leads to the infection of both human and mouse cell lines [11]. As a result, the approach to detecting viral contamination is customized and differs between labs.

Antiviral medications, such as reverse transcriptase inhibitors, are effective treatments for viral infections [12]. Furthermore, researchers can opt to employ immunoneutralization or serial dilution techniques [13, 14]. However, purifying infected cell lines is rarely done because of safety hazards. It takes far less effort to sterilize the biological material and seed the cells onto a fresh extracellular matrix. The purification process is warranted exclusively for unique hybrids or essential foundation lines. Consequently, a problem arises as the disposal of contaminated materials frequently results in substantial financial losses. Since the exact source of infection is rarely identifiable, it becomes necessary to discard the majority of consumables. To avoid this situation and minimize expenses, it makes sense to utilize a diagnostic system to sort clean cultures from contaminated ones. Given the unique nature of viral infections, large institutions must re-evaluate their diagnostic strategies for identifying laboratory material contamination.

PRINCIPLES OF ORGANIZATION OF LABORATORY DIAGNOSTIC SYSTEMS

There is an urgent need to address both the selection of methodologies for contaminant detection and the transition from foreign testing platforms to domestic Russian systems. Data from the University School of Chemistry*Bio*Plus project reveals a heavily import-dependent market: the Russian Federation produces fewer than 50 types of contaminant detection kits, representing roughly 7% of the market share, compared to more than 250 types manufactured in Germany alone. Therefore, the establishment of domestic diagnostic systems represents a critical milestone for the Russian market. As illustrated in fig. 1, correlating the financial cost of diagnostic systems with their operational labor costs, sensitivity, and specificity reveals that polymerase chain reaction (PCR) methodology offers the most efficient solution for contaminant screening in major organizations.

To confirm and identify contamination, the algorithm in fig. 2 must combine PCR with microscopy, which prevents fungal overgrowth in the culture. If working within the same facility, it is imperative to update the range of viruses whenever along new cell lines are introduced.

However, for large research facilities with multiple independent labs, an isolated biosafety strategy is unsustainable. The elevated frequency of biomaterial transfer, along with the utilization of shared instrumentation and inherent human error, introduces the threat of undetected contamination. Given these conditions, localized observation must be superseded by a comprehensive, end-to-end control protocol, and details concerning the identification of a specific pathogen must be distributed throughout the entire infrastructure. The transition from the situational eradication of pathogens to proactive biological risk management necessitates a standardized algorithm for contamination detection and prompt dissemination of alerts regarding emerging biohazards across laboratory networks.

CENTRALIZED DATABASE AS A CORE ASSET

Developing a unified PCR testing system requires centralizing the core workflow phases: sample preparation, reaction setup, amplification, and result analysis. Research indicates that establishing distinct operational zones and implementing a one-way workflow for samples are critical measures to minimize the risk of contamination. Within the framework of the institute, this requires a unified material-handling policy that prohibits the transfer of samples from “dirty” to “clean” areas [15]. This regulation will minimize false positive results, which usually stem from contamination by amplification products or cross-contamination. It also helps account for unmonitored reaction inhibition and extraction issues [16–18]. To improve accuracy, we need both rigorous quality controls and the establishment of shared diagnostic systems, whether for individual institutions or networks of laboratories.

Furthermore, establishing a shared diagnostic system requires accounting for variations in local protocols and staff expertise. Effective contamination incident management requires standardized training and regular audits, which are just as critical as technical solutions. A centralized PCR diagnostic system should offer methodological guidance and expert consultation to institute laboratories, along with testing services. This ensures standardized procedures and fosters a stronger culture of quality [19].

Research in contamination control and laboratory management demonstrates that systematic registration and analysis of errors enable the identification of systemic flaws, ultimately optimizing protocols and preventing their recurrence. Within the institute, a shared database will form the basis for information exchange between laboratories, helping to resolve recurring issues and lower operational costs (fig. 3).

Therefore, a single database integrated into a standard data exchange system becomes much more than just an archive. It has become the foundation of the quality management system and the hub for coordinating the institute’s laboratories. Integrating information flows into a single digital architecture enables comprehensive, end-to-end management of all biomaterial processes. Implementing this framework will guarantee the ongoing detection of biological systems and possible pollutants. Because false results and cross-contamination now present widespread challenges rather than isolated incidents, rigorous verification and assessment have become essential. Exchanging samples and sharing equipment causes these errors to accumulate. At the institutional level, these issues escalate into widespread, systemic problems that severely threaten the validity of scientific and clinical results. Ultimately, a centralized database serves as the foundation for research, tracking its status while preventing the spread of dangerous biological systems to ensure total biosafety and data integrity.