Enabling global-scale nucleic acid repositories through versatile, scalable biochemical selection from room-temperature archives

Domain: neuroscience
Published: 2026-02-14
Editorial outcome: minor_revision

Abstract
Conventional storage and retrieval of nucleic acid specimens, particularly unstable RNA, rely on costly cold-chain infrastructure and inefficient robotic handling, inhibiting large-scale nucleic acid archives needed for global genomic biobanking. We introduce a scalable room-temperature storage system with minimal physical footprint that enables database-like queries on encapsulated, barcoded, and pooled nucleic acid samples. Queries incorporate numerical ranges, categorical filters, and combinations thereof, advancing beyond previous demonstrations of single-sample retrieval or Boolean classifiers. We evaluate this system on ninety-six mock SARS-CoV-2 genomic samples barcoded with theoretical patient data including age, location, and diagnostic state, demonstrating rapid, scalable retrieval. We further demonstrate storage and sequencing of human patient-derived nucleic acid samples, illustrating applicability to clinical genomic analysis. By avoiding freezer-based storage and retrieval, this approach scales to millions of samples without loss of fidelity or throughput, enabling large-scale pathogen and genomic repositories in under-resourced or isolated regions of the US and worldwide.

Introduction
Large-scale collection, transport, storage, and retrieval of nucleic acids is essential to enable numerous advanced biotechnological application areas, including population-scale disease tracking1, precision genomic medicine2–4, forensics5, and global ecological record-keeping6,7. In particular, genomic DNA and RNA collected from large and diverse patient cohorts empower both pathogen tracing and genomic medicine by enabling the prediction of disease-onset likelihood, as well as informing personalized treatment plans4,8–12. In general, intact native nucleic acid samples provide the most complete representation of genomic information, including epigenetic marks, such as methylation patterns in DNA and genomic viral RNA, as well as modifications to mRNA and lncRNA13,14. Comprehensive nucleic acid analyses rely on both short-read and long-read sequencing platforms to generate the most detailed and accurate genomic data. These platforms, along with gold standard assays like mass spectrometry for analyzing epigenomic and RNA modifications15, can be both time- and cost-prohibitive. As a result, most samples are preserved in cold storage for future analysis rather than being fully processed immediately. For example, the UK Biobank has collected approximately 500,000 patient samples, but genetic characterization has been limited to a large pre-selected set of genetic markers. Portions of the original blood samples were preserved at either −80°C or −180°C to enable future analyses16,17.

This long-term storage strategy offers several advantages. It reduces upfront costs, allows researchers to take advantage of future improvements in sequencing technology, and enables sequencing efforts to focus on samples that prove relevant to research questions of interest, which may only become apparent long after sample collection. Yet despite its advantages, storage of intact nucleic acid samples is also a costly option. Samples require energy-intensive cold-chain infrastructure for sample preservation and, once sequencing is desired, transport to centralized analysis facilities18–20. This infrastructure burden is particularly challenging for RNA samples, which are highly susceptible to degradation without stringent preservation protocols21,22. Even after reaching analysis facilities, samples may require prolonged low-temperature storage while awaiting comprehensive analyses. Further, the throughput and efficiency of sample recall from large-scale, automated freezer systems are limited by mechanical factors such as robotic automation speed.

As epidemiology, pathogen surveillance, personalized medicine, and ecological conservation efforts attempt to scale to worldwide sample collection, the aforementioned challenges create significant technological barriers that limit access to nucleic acid samples from under-resourced regions both in low- and high-income nations23. This contributes to severely limited participation in rare diseases research, where understanding complex genetic traits and disease associations requires the analysis of tens of millions or more intact DNA and RNA samples from diverse global populations24–28. Similarly, pathogen monitoring and ecological preservation efforts at a global scale become prohibitively cumbersome and costly. Thus, there is an urgent need for low-cost, low-energy, and scalable storage infrastructures that preserve DNA and RNA at the point of collection while simultaneously enabling ambient transport and efficient sample retrieval for downstream genomic analyses.

Traditional biosample storage methods, which rely on barcoded tubes stored in freezers or liquid nitrogen tanks, face significant challenges in cost and practicality as collections scale into the millions. For these vast biosample databases, sophisticated systems are required for efficient sample search and retrieval, in addition to continuous energy consumption and high-cost storage infrastructure. While automation partially alleviates these barriers, these biosample databases remain limited by their low storage density and use of sequential rather than parallel or multiplexed sample access.

In contrast, molecular-based approaches enable in principle the pooling of millions to billions of unique nucleic acid biosamples per tube, effectively creating a highly dense biosample database. Retrieval of specific samples or sets of related samples can be achieved using biochemical approaches such as PCR29, magnetic pulldown30, or fluorescence-activated sorting (FAS)31,32 that use molecular labels including primers, affinity tags, and fluorescent dyes. Primers and affinity tags are often made from DNA, capitalizing on the innate specificity of DNA hybridization to ensure precision and scalability of retrieval. These methods simultaneously process the entire pool of biosamples, which improves retrieval efficiency significantly by executing millions to billions of concurrent molecular search and retrieval operations in solution to far surpass the capabilities of conventional manual or robotic search-and-retrieval of individual tubes.

For example, we previously demonstrated a system of silica-encapsulated biosamples labeled with DNA barcodes. The DNA barcodes encoded Boolean metadata about each sample, such as the species of origin, with each barcode sequence associated with a single metadata value (e.g., E. coli or Homo sapiens). This framework facilitated precise identification of samples using fluorescent probes to detect each barcode, while silica encapsulation effectively preserved biosamples at room temperature and prevented unintended interactions with the biosamples that could interfere with biochemical retrieval. With this framework, we demonstrated Boolean retrieval in two studies: one using a prototypical image database stored in plasmid DNA32, and another with a pool of genomic DNA and RNA samples31. Retrieval was demonstrated to have high specificity, with successful retrieval of target samples comprising just 1 in 10^6 of the total sample pool32. However, this and prior molecular labeling systems29,30,33 remain limited to simple Boolean queries and do not effectively perform numerical range searches (e.g., for time or age ranges) or categorical searches that are required for efficient, large-scale biospecimen retrieval and are routinely performed on modern digital databases using Structured Query Language (SQL).

As an example, consider labeling samples with barcodes to indicate patient ages between 0 and 99. Previous barcoding schemes would associate each possible age value with one of 100 distinct barcode sequences31,32. A simple numerical range query for samples from patients aged 50–74 would require checking for the presence of 25 different barcodes, requiring 9 stages of fluorescent sorting, assuming up to three barcodes may be checked at each stage. Moreover, the queries essential for genomics and epidemiological applications routinely involve selection based on multiple patient criteria, diagnostic conditions, and/or time periods, further compounding the number of sorting stages required. Considering the time taken and selection error compounded at each sorting stage, this type of barcoding scheme quickly becomes impractical for complex queries. This renders these prior molecular labeling systems unsuitable for large-scale genomic databases, which contain tens to hundreds of millions or even billions of samples, and where such queries become essential.

To enable biosample database implementations that approach the capabilities of modern digital databases, in the current work, we develop a scalable biosample database that permits efficient metadata queries for numerical ranges, such as ranges of dates or ages; categories, such as cities or countries; and previously demonstrated Boolean classifications, such as symptomatic/asymptomatic. Central to our approach is the introduction of a type-aware schema that maps each metadata field to a compact, composable set of barcodes (rather than one barcode per value), enabling these queries via barcode presence readout. In this framework, sets of barcodes efficiently encode values across distinct data types, providing a high-level abstraction over the raw binary information encoded by the presence or absence of each barcode sequence. Using a synthesized database of model SARS-CoV-2 genomes, we demonstrate the effectiveness of our query system in a simulated pathogen outbreak scenario, where passengers entering a major international airport are comprehensively swabbed for SARS-CoV-2 to track the pandemic (Fig. 1a). Each sample simultaneously encodes age, vaccination status, presence of symptoms, and flight information (flight number, month and year, and place of origin). We performed retrospective epidemiological and immunological analyses with queries of increasing complexity, including queries for a particular health status, for three distinct age ranges, and for simultaneous matches to criteria for date range, location, and health status. These examples illustrate a general-purpose SQL-like query language, permitting arbitrary logical expressions composed of numerical range, categorical, and Boolean metadata criteria. This work thereby demonstrates a scalable storage system that supports the expressive query capabilities of modern digital databases (Fig. 1b), while eliminating cold-chain logistics through silica encapsulation for long-term RNA sample preservation at room temperature31,32. This framework addresses bottlenecks in nucleic acid storage and retrieval, offering broad applications in molecular diagnostics, pathogen surveillance, and ecological preservation.

Results
Database construction
Ninety-six mock patient samples were separately encapsulated in 5 µm silica microcapsules (Fig. 1a). These microcapsules simulated samples drawn from passengers flying into Logan International Airport in Boston, Massachusetts, and subsequently stored archivally for future diagnostic or epidemiological testing, if needed. Each sample consisted of at most one variant of the SARS-CoV-2 genome using either synthetic P.1 and B.1.1.7 variants and a unique internal 85-nt barcode whose purpose was to aid validation of microcapsule retrieval by making possible downstream identification of de-encapsulated microcapsules in a pool. Variants were quasi-randomly assigned to each sample, with approximately a 15% chance of some variant being present. In contrast to our previous work31,32, where each encapsulation reaction contained a single nucleic acid species such as plasmid DNA32, or various other individual nucleic acids like mammalian and bacterial genomic DNA, total RNA from human cells, or SARS-CoV-2 RNA31, the current work employed co-encapsulation of multiple nucleic acid types within each reaction. Specifically, the nucleic acids introduced into each encapsulation reaction consisted of ~10^15 copies of 85-nt ssDNA internal barcodes that uniquely identified each patient sample for purposes of retrieval validation and, for designated samples, ~10^7 copies of 5kb fragments of synthetic SARS-CoV-2 RNA. Because the encapsulation process relied solely on charge interactions between the negatively charged phosphate backbone of nucleic acids, we expected successful incorporation of all nucleic acids irrespective of length or type (Supplementary Fig. 1), as previously demonstrated31. Subsequent validation using qPCR and RT-qPCR confirmed efficient nucleic acid encapsulation and de-encapsulation, yielding an estimated average of approximately 1 copy of the SARS-CoV-2 genome and ~10^8 copies of the internal barcode per microcapsule.

Database labeling and querying
The ideal SARS-CoV-2 genomic database would label each sample with metadata features, such as its unique identifier, patient health status, sample acquisition date, and flight origin. For our example use case, several features were chosen to describe a variety of relevant metadata, including patient age and month and year of the arrival flight, which are examples of numerical metadata; vaccination status, which is an example of Boolean metadata; and flight number and city of origin, which are examples of categorical metadata. For each microcapsule, each feature value was encoded into a set of barcodes to be displayed on its exterior (Fig. 1a), with a total of either 13 or 14 unique 25-nt ssDNA barcodes per microcapsule. These barcodes were derived from a library of 240,000 orthogonal 25-mers34, which were computationally designed and experimentally validated for orthogonality to ensure minimal cross-hybridization even when many distinct barcode sequences were used within a pooled system. Differing encoding strategies were employed based on the type of metadata (numerical, Boolean, or categorical), which enabled type-specific queries of each feature, such as queries for numerical ranges. More detail about each encoding and retrieval strategy is given in Methods.

For numerical metadata, we encoded each numerical value as a sequence of digits in a mixed-radix representation, corresponding to a sequence of barcodes with one barcode per digit. Queries for an exact numerical match are performed by probing for every barcode in its representation, while ranges of various sizes are possible by omitting one or more of the less significant digits (Fig. 2). Categorical metadata were encoded using distinct combinations of k barcodes for each possible value, so that the value of k determined the number of barcodes required to identify a sample’s specific feature value. This approach scales to accommodate an extensive number of feature values. Boolean metadata, on the other hand, were straightforward, with the presence or absence of a single barcode indicating the feature’s state.

When performing a query, the search string is first translated into a DNA barcode list (Fig. 2). This list not only facilitates the query but also informs the selection of unique dyes needed for FAS. For NOT logic, the barcode that denotes the unwanted condition is tagged with a fluorescent dye. During sorting, microcapsules that do not show this fluorescence are selected, thus excluding the condition indicated by the dye. OR logic employs a single dye for all relevant barcodes, selecting samples with the matching dye. For AND logic, each query barcode is tagged with a distinct dye, and only samples displaying all unique dyes are selected. This intricate logic necessitates a careful selection of dyes and a strategic grouping of search parameters, ensuring precise sample retrieval.

Immunological case study
To demonstrate the application of a large-scale molecular database with advanced search query capabilities, we performed several database queries on the synthetic database of 96 SARS-CoV-2 samples hypothetically collected from airplane passengers entering Boston, MA. We designed search queries to demonstrate the breadth of the queries enabled by the sample labeling approach, as well as to show how an actual database of this type could be used to answer valuable retrospective epidemiological and immunological questions.

We began with the immunological question of whether specific SARS-CoV-2 variants were present in asymptomatic passengers. To answer this question, we used a query for when the Boolean feature symptomatic was false. For each sample, the barcode bc_symptomatic was present when this feature was true, and absent when this feature was false. Thus, our query NOT symptomatic should retrieve exactly those samples not displaying this barcode.

The presence of the barcode bc_symptomatic on each microcapsule was determined using a fluorescent probe combined with fluorescence-associated sorting (FAS). However, in comparison to previous work using FAS for barcode detection on microcapsules31,32, the number of distinct barcodes attached per microcapsule was increased from 3 to up to 14, leading to a proportional decrease in the copy number of each barcode per microcapsule. To compensate for this copy number reduction, we used fluorescence amplifiers routinely used to detect low copy numbers of RNA in cells in flow cytometry35 (Fig. 3a). The pool of microcapsules was mixed with this amplifier probe and subsequently passed through a FAS instrument, which generates a stream of droplets. Fluorescence from each droplet was measured, and selected populations were defined using fluorescence intensity to distinguish microcapsules with and without the fluorescent probe attached. The number of particles measured for each sample showed a bimodal distribution between the retrieved and non-retrieved microcapsules for the NOT symptomatic query (Fig. 3b). The low-intensity population was separated from the rest of the pool for subsequent de-encapsulation and sequencing.

To validate correct retrieval, sequencing reads of the 85-nt internal barcodes were matched to the known sequences to identify the samples in the sequencing data. We quantified retrieval performance using several metrics. Receiver operator characteristic (ROC) analysis on the internal barcode read counts was performed to compute the area under the ROC curve (AUC). The AUC value provides a threshold-independent measure reflecting the system’s overall ability to rank target samples above non-target samples, with 1.0 indicating every target sample is enriched relative to every non-target sample, and 0.5 indicating performance no better than random chance (i.e., a randomly selected target sample is equally likely to be enriched or not relative to a randomly selected non-target sample). For the NOT symptomatic query, where the target (non-symptomatic samples) constituted a high proportion (93 of 96 samples, or ~96.9%) of the initial pool (Fig. 3b), the mean AUC over 3 replicates was consistently high, approaching 1.0 with increasing read depth (Fig. 3c). Figure 3d further details the retrieval performance by presenting the distributions of internal barcode read counts for the NOT symptomatic query at different sequencing depths. These distributions indicate that read counts from true positive barcodes were generally higher than those from false positive barcodes, with improved separation between the two distributions as sequencing depth increased.

Next, we demonstrated how range queries on patient age could be used to explore if certain age groups were more susceptible to different SARS-CoV-2 variants36. Age metadata was encoded numerically using a base-5 representation of three digits with place values 25, 5, and 1. Our conversion of numerical representations to barcodes is described in more detail in Supplementary Fig. 6. We considered three age range queries of different size: age = 0 (Fig. 4a), 15 ≤ age < 20 (Fig. 4e), and 50 ≤ age < 75 (Fig. 4i). Range queries of other sizes or positions are also possible by combining ranges using AND, OR, and NOT logic. For the narrow query age = 0, we selected samples labeled with barcodes bc_age_x25_seq0, bc_age_x5_seq0, and bc_age_x1_seq0 using the same type of multi-stranded branched probes as previously described, labeled with fluorophores Atto 565, Alexa Fluor 647, and Alexa Fluor 750, respectively (Fig. 4b). For the moderate-range query 15 ≤ age < 20, we selected samples labeled with barcodes bc_age_x25_seq0 and bc_age_x5_seq3, using probes labeled with fluorophores Atto 565 and Alexa 647 (Fig. 4f). For the query of broad range 50 ≤ age < 75, we selected samples labeled with the barcode bc_age_x25_seq2 using a probe labeled with fluorophore Atto 565 (Fig. 4j).

In each case, sequencing the internal 85-nt barcodes confirmed accurate sample retrieval. ROC analysis yielded perfect AUC values (1.0) across all read depths for the first two queries, age = 0 (Fig. 4c) and 15 ≤ age < 20 (Fig. 4g). The broader query, 50 ≤ age < 75, also showed good performance, with AUC values improving from approximately 0.8 to over 0.9 with increasing read depth (Fig. 4k). Examination of the internal barcode read count distributions (Fig. 4d, h, l) provided further insight. For the narrower queries (age = 0, Fig. 4d; and 15 ≤ age < 20, Fig. 4h), a clear and consistent separation was observed between the higher read counts of true positive barcodes and the very low read counts of false positive barcodes across all sequencing depths, indicating excellent discrimination. The broader query (50 ≤ age < 75, Fig. 4l) exhibited more overlap between true positive and false positive read count distributions compared to the narrower queries, particularly at lower sequencing depths.

Epidemiological case study
We then sought to demonstrate how a composite query involving multiple metadata types can be used to understand the transmission of SARS-CoV-2 infection from specific areas, date ranges, and flight cities37. For the epidemiological case study, we illustrated two aspects of our database: first, efficient representation and querying of categorical features; and second, the composition of several smaller queries into arbitrarily complex logical expressions. This was performed via the retrieval of all samples for passengers flying from Chicago between July and September 2020, who were either symptomatic or unvaccinated. This was equivalent to the query (symptomatic OR NOT vaccinated) AND flight_city = Chicago AND 6 ≤ arrival_month ≤ 8 AND arrival_year = 2020, which combines queries on two numerical features, one categorical feature, and two Boolean features. Such a query necessitated examining eight barcodes: bc_vaccinated, bc_symptomatic, bc_city_seq0, bc_city_seq3, bc_city_seq4, bc_flight_month_x3_seq2, bc_flight_year_x10_seq2, and bc_flight_year_x1_seq0, making it one of the most complex queries tested on any molecular database to date, both semantically and in terms of the number of barcodes tested. To further exhibit the flexibility of our approach to a variety of fluorescent channels and probe design methodologies, we selected a new set of dyes for this query, which modified the bandwidth of our dye markers (Fig. 5a). Specifically, we transitioned from Atto 565, Alexa Fluor 647, and Alexa Fluor 750 to Atto 488, Atto 565, and Alexa Fluor 647. The reduced brightness of Atto 488 relative to the other dyes necessitated the use of branched probe designs that amplify fluorescence signals, similar to branched probe designs used to improve the relative brightness of low-copy targets in cell imaging38,39. This strategy allowed the amplification of the net fluorescent signal by increasing the number of dye markers per barcode.

This query was performed over three FAS passes, using probes with fluorophores Atto 488, Atto 565, and Alexa Fluor 647 for bc_vaccinated, bc_symptomatic, and bc_flight_month_x3_seq2, respectively, for the first pass (Fig. 5b, top). The sorted populations were stripped of their fluorescent probes using a denaturation buffer. For the second round of selection, fluorophores Atto 488, Atto 565, and Alexa Fluor 647 were used for selecting bc_city_seq0, bc_city_seq3, bc_city_seq4, respectively (Fig. 5b, middle). Again, the previous probes were removed prior to subjecting the sorted population to the next round of selection. For the final FAS pass, Atto 488 and Atto 565 were used for bc_flight_year_x10_seq2 and bc_flight_year_x1_seq0, respectively (Fig. 5b, bottom). Sequencing of the 85-nt internal barcodes after all three passes indicated correct enrichment of all on-target samples (AUC = 1.0) (Fig. 5c). Consistent with this performance, the internal barcode read count distributions (Fig. 5d) showed a clear and consistent separation between the high read counts from the on-target samples and the low read counts from the off-target samples across all sequencing depths. The success of this demonstration illustrates two important features of our approach: first, the ability to implement a single molecular database query that describes criteria spanning many features, such as numerical ranges, categorical values, and multiple Boolean conditions; and second, the ability to split complex queries over several FAS passes without loss of retrieval fidelity.

SARS-CoV-2 sequencing
Identifying the dominant SARS-CoV-2 variant is essential for assessing the virulence of emerging strains, forecasting outbreaks, and expediting vaccine development40. We aimed to identify the predominant SARS-CoV-2 variant in our queries, given that our samples were encapsulated with either the Alpha or Gamma variants. After sorting 100,000 to 700,000 microcapsules for each selection, we sequenced samples that showed a positive result from a specifically designed end-point tiling amplicon PCR for SARS-CoV-2 (ARTIC protocol)41 yielding the expected distribution of fragment lengths (Supplementary Fig. 3). Computational demultiplexing using Freyja42 revealed that all samples showed mostly Alpha variants followed by Gamma variants and other variants that were not assigned by Freyja (Fig. 6a). In all cases, the ratio of expected Alpha to Gamma abundance: 8:3 for selections from Fig. 3, 2:1 for selections from Fig. 4c, and 1:0 for selections from Fig. 5, closely matched the measured abundances in Fig. 6a, further providing support to the high retrieval precision observed using an orthogonal sequencing approach.

To demonstrate the viability of encapsulation for storage and recovery of clinical SARS-CoV-2 samples, we applied our approach to five distinct patient-derived samples. Each sample contained different sub-lineages of the Omicron variant, allowing us to gauge our ability to detect small variations in the viral genomes present during encapsulation through the comparison of called variants of each sample with and without encapsulation. For each sample, we successfully recalled the sub-lineages of Omicron variants (Supplementary Table 2). Further examination of the data indicated that the precision and recall of variant calling for the encapsulated samples were affected by the sequencing coverage (Fig. 6b), which we attribute to the low copy numbers of SARS-CoV-2 retrieved from encapsulation resulting in amplicon dropouts (Supplementary Fig. 4 and Supplementary Fig. 5). The dropouts observed in the low-copy clinical samples (Supplementary Fig. 4 and Supplementary Fig. 5) appeared stochastic and were not consistently biased against longer amplicons, a pattern characteristic of low template input effects rather than systematic RNA degradation during processing43–45. Crucially, despite these low-template effects in some clinical samples, overall RNA integrity was sufficient for successful variant identification using the short-read ARTIC protocol for both our synthetic retrieved samples (Fig. 6a) and the recalled Omicron sub-lineages from the clinical samples (Supplementary Table 2).

Methods
General materials
All DNA oligonucleotides (oligos)—including internal barcodes (Supplementary Data 2), splint adapters, 5’-amino-modified DNA barcodes (Supplementary Data 3), dye-labeled probes, preamplifier probes, amplifier sequences (Supplementary Data 1), master forward and reverse primers (Supplementary Table 1), random hexamers, and 20-mer oligodeoxythymidine—were synthesized and processed by Integrated DNA Technologies (IDT). Specifically, 5’-amino-modified barcodes in Echo 384 polypropylene microplates and internal barcodes in 96-deep well plates were purchased as desalted and delivered in nuclease-free water at 1000 μM and in 1× TE at 500 μM, respectively. Dye-labeled probes were selected for their brightness (quantum yield × molar absorption coefficient at the excitation wavelength for each detection channel) while minimizing fluorescence spillover, with the final selections being Atto 488, Atto 565, Alexa Fluor 647, and Alexa Fluor 750. These dye-labeled probes, along with adapter sequences and branch sequences, were received in 1× TE at concentrations of 100 μM, 33 μM, and 10 μM, respectively. The master primers were desalted and resuspended at 100 μM with nuclease-free water, while random hexamers and 20-mer oligodeoxythymidine were purified using ion-exchange high-performance liquid chromatography and resuspended at 50 μM with nuclease-free water. SARS-CoV-2 RNA controls were sourced from Twist Bioscience with catalog numbers 103909 and 104044. All oligos were stored at –20°C. Silica particles of 5 μm diameter with hydroxy-terminated surfaces (catalog number: DNG-B017) were obtained from Creative Diagnostics, and N-[3-(Trimethoxysilyl)propyl]-N,N,N-trimethylammonium chloride (TMAPS; 50% methanol; catalog number: H66414) was acquired from Alfa Aesar. Chemicals such as tetraethylorthosilicate (TEOS), N-(2-aminoethyl)-3-aminopropyltrimethoxysilane (AEAPTS), N-methyl-2-pyrrolidone (NMP), isopropanol, and ethanol were sourced from MilliporeSigma, bearing catalog numbers: 131903, 8.19172, 270458, 278475, and 459836, respectively. DBCO-PEG5-tetraester (1260-10) and azidoacetic acid N-hydroxysuccinimide ester (1070-100) were from Click Chemistry Tools. Additionally, carbonate buffer (500 mM, pH 9.0; catalog number: J63899.AK) and saline sodium citrate (SSC, 20×; catalog number: 15557044) were purchased from Thermo Fisher, while dextran sulfate (50% in water; catalog number: S4030) came from MilliporeSigma, 10% Tween 20 from VWR (catalog number: 97063-980), formamide from VWR (catalog number: 97062-006), and 5 M sodium chloride from VWR (catalog number: 97062-858).

Internal barcode generation
A subset of primers, 700 in total, from the validated 240,000 primer library34 was checked for alignment against SARS-CoV-2 using BLAST55. The alignment XML output file from BLAST was then parsed to create a list of primers orthogonal to the SARS-CoV-2 genome. The resulting primer list was further filtered by selecting primers with 60–65°C melting temperature using the melting temperature module in Biopython (version 1.85).

Internal barcodes were generated by first picking a master primer pair from the filtered primer list, forward: 5’–GGCTATGAGACTGTTCGCTAATCAC–3’ and reverse: 5’–CCCTTTGTGGGCACAGTTTAGTCTC–3’, which flanked a unique barcode taken from the primer list. Five-nucleotide randomer spacers (N) were also added between the master primers and the unique barcode to increase the sequence diversity of the internal barcodes for downstream sequencing. Together, the 85-nucleotide internal barcode sequence structure is GGCTATGAGACTGTTCGCTAATCACNNNNNUUUUUUUUUUUUUUUUUUUUUUUUUNNNNNCCCTTTGTGGGCACAGTTTAGTCTC, where U is the unique barcode sequence. The full list of internal barcode sequences is given in Supplementary Data 2.

96-well encapsulation
Ammonium-functionalized silica particles were prepared by hydrolyzing 100 μl TMAPS in a suspension containing 100 mg of silica particles in ethanol at room temperature for 24 h under constant agitation (1500 rpm) using a BioShake iQ thermal mixer, followed by three sequential washes in ethanol. The ammonium-functionalized particles are stored in isopropanol.

For each well in a Nunc 96 deep-well polypropylene plates (Thermo Fisher; catalog number: 278752), a total of 2 mg of ammonium-functionalized silica particles and 800 μl of 0.1% (v/v) Tween 20 in nuclease-free water were added. A total of 5 nanomoles of each internal barcode were added to their respective wells. Ten million copies of SARS-CoV-2 standards were added to each randomly selected well; the list of SARS-CoV-2 variants of concern (VOC) associated with each sample are given in Supplementary Data 5. A volume of 10 μl of TMAPS and 5 μl of TEOS were then added to each well. The plate was then covered with a chemically resistant silicone mat (Axygen; catalog number: AM-2ML-RD-S) and shaken for four days at 1500 rpm using a BioShake iQ thermal mixer (BulldogBio; catalog number: 1808-0506).

Encoding of features with DNA barcodes
A full list of sequences for each DNA barcode is given in Supplementary Data 3. The mapping between feature values and DNA barcodes is given in Supplementary Data 6. For each feature, a set of barcodes was allocated from which a subset was drawn to encode each feature value. The sets of barcodes for each feature were disjoint from each other. The encoding strategy for feature values differed based on the type of the metadata (numerical, Boolean, or categorical), which enabled type-specific queries of each feature, such as queries for feature values matching particular numerical ranges.

Numerical features. Numerical features were encoded using a mixed-radix number system (i.e., a sequence of digits with the base allowed to vary between positions) (Supplementary Fig. 6a). A position with base n was allocated n distinct barcode sequences, one for each of the n possible digit values at that position. Thus, a feature value represented by k digits was encoded on each microcapsule with a collection of k distinct barcodes. Ranges of varying size could be specified by allowing some number of the least significant digits to vary (i.e., using wildcards at these digits) (Supplementary Fig. 6b). Experimentally, any numerical range specified in this manner can be retrieved by omitting the corresponding complementary probes during sorting. The base at each position was chosen to provide a good compromise between feature width (the number of barcodes required on a microcapsule to represent its feature value) and the number of barcodes that needed to be allocated for this feature.

Boolean features. Each Boolean feature was encoded using a single barcode assigned to that feature, similar to barcoding previously used to indicate image content in a database of images32. When the value of a Boolean feature was TRUE for a sample, the barcode was displayed on the microcapsule; a value of FALSE was indicated by the absence of that barcode.

Categorical features. For each categorical feature, we used a combinatorial number system56 to associate each possible feature value with a distinct k-combination drawn from a chosen set of n barcodes, where k is the number of distinct barcodes used to represent this feature on each microcapsule. The number of possible feature values that may be represented is (n choose k), which grows rapidly with both n and k. The combinatorial number system provides a method to associate each possible k-combination with a unique integer value between 0 and (n choose k) - 1. Each feature value was assigned a unique numerical index in that range, from which the corresponding k-combination of barcodes was determined. The values of k and n were chosen to provide a reasonable compromise between feature width (the number of barcodes required on a microcapsule to represent its feature value) and the number of barcodes that needed to be allocated for this feature.

Barcoding of individual samples and pooling
After encapsulation, the plate was centrifuged for 1 minute at 1000 × g. The supernatant was removed, then backfilled with 1000 μl of 0.1% (v/v) Tween 20. A volume of 10 μl of AEAPTS was then added. The plate was then covered with a chemically resistant silicone mat and shaken for 1 day at 1500 rpm using a BioShake iQ thermal mixer. To wash the encapsulated microparticles after amino modification, the plate was centrifuged for 1 min at 1000 × g. The supernatant was removed and then backfilled with 1000 μl of NMP. The washing step was repeated thrice and finally resuspended with 500 μl of NMP. A mass of 1 mg of azidoacetic acid N-hydroxysuccinimide ester was added to each well. The plates were then re-sealed with a chemically resistant silicone mat and shaken for 3 h at 1500 rpm using a BioShake iQ thermal mixer. After azide modification, the previous wash steps were repeated, and the microparticles were finally resuspended in 500 μl of NMP. A mass of 0.5 mg of DBCO-PEG5-tetrafluorophenyl ester was added to each well, re-sealed with a chemically resistant silicone mat, and shaken for 5 h at 1500 rpm using a BioShake iQ thermal mixer. The plate was washed with NMP thrice and then resuspended with 200 μl of NMP.

Barcode combinations were dispensed in a 96-well plate using an Echo 550 liquid handler and then transferred to the 96-deep-well plate containing the encapsulated microparticles. To each well of the 96 deep-well plates was added 800 μl of 50 mM carbonate buffer. The plate was re-sealed with a chemically resistant silicone mat and then shaken for 12 h at 1500 rpm using a BioShake iQ thermal mixer. The barcoded microparticles were centrifuged at 1000 × g then the supernatant was removed. The resulting pellet was washed with 20 mM Tris, 1 mM EDTA, and 0.1% (v/v) Tween 20 through repeated centrifugation, removal of the supernatant, and redispersing of the supernatant with 20 mM Tris, 1 mM EDTA, and 0.1% Tween 20 for three times. The microparticles were finally redispersed in 1000 μl of 20 mM Tris, 1 mM EDTA, and 0.1% (v/v) Tween 20. A volume of 500 μl from each well was taken and pooled together to create the sample library. A full list of the metadata associated with each sample is given in Supplementary Data 4.

Selection of microparticles
An aliquot of 500 μl of the sample library was placed in a 1.5 ml tube and then centrifuged at 1000 × g to sediment the microparticles. The supernatant was removed, then the microparticles were re-dispersed with 200 μl of hybridization buffer (10× SSC, 10% (v/v) dextran sulfate, 10% (v/v) formamide, and 0.05% (v/v) Tween 20). The hybridization buffer was optimized to create stringent conditions that minimized non-specific binding by destabilizing weak or mismatched duplexes. Separately, equivolume of barcode probes, adapters, and fluorescent probes, and 1× SSC were pre-hybridized using the following method: 98°C for 10 s, 40°C for 5 min, 20°C for 2 min. A volume of 20 μl of prehybridized probe solutions were then added to the microparticle suspension. The resulting mixture was shaken at 1500 rpm for 15 min at 35°C using a BioShake iQ thermal mixer, then centrifuged at 1000 × g to sediment the microparticles. The supernatant was removed, and the microparticles were re-dispersed in 1000 μl of the sorting buffer, composed of 1× SSC and 0.05% (v/v) Tween 20, to further remove any non-specifically bound probes. The microparticles sedimentation and washing steps were repeated thrice. The microparticles were finally resuspended in 1000 μl of sorting buffer. The fluorescently labeled microparticles were sorted using a Sony SH800 sorter equipped with a 100 μm sorting chip.

FAS were performed using a Sony SH800 cell sorter with a 100-µm sorting chip and three fluorescence detection channels. Forward and side scatter were first used to gate singlet microcapsules, excluding debris and aggregates. Fluorescence intensities for Atto 488, Atto 565, Alexa Fluor 647, and Alexa Fluor 750 were analyzed in two-dimensional scatter plots for each dye combination. Gates were established manually using unstained and single-stained controls to define positive and negative populations for each barcode-specific probe (Supplementary Fig. 15). All gating thresholds and instrument settings were held constant for every query and replicate. Because fluorescence profiles were indistinguishable between sorts performed under identical conditions, a representative gating dataset was recorded once and used as the reference for all subsequent experiments.

In the multi-pass selection process, existing fluorescent probes from previously sorted populations were meticulously removed before initiating subsequent rounds. Populations from an earlier selection were initially centrifuged in a 1.5 ml tube at 1000 × g for 20 s, after which the sheath buffer was gently discarded. Next, 1000 µl of a denaturation buffer, comprised of 0.2 M NaOH in 90% formamide and pre-heated to 70°C, was added. This mixture was swiftly vortexed for 5 s and then incubated at 70°C in a BioShake iQ thermal mixer for 15 min. Following this incubation period, another 20-second centrifugation at 1000 × g was performed then denaturation buffer was discarded. Then, 1000 µl of a denaturation wash buffer, composed of 0.05% Tween 20 and pre-heated to 70°C, was added. After a brief 5-second vortex and a 20 s centrifugation at 1000 × g, the denaturation wash buffer supernatant was carefully siphoned off. Finally, 200 µl of hybridization buffer was added, preparing the sample for the next selection phase.

Sorted microparticles were de-encapsulated using 10 μl of electronics-grade 5:1 buffered oxide etch (VWR, catalog number: JT5192-3) and then diluted to 50 μl with nuclease-free water. The released samples were immediately used for Illumina sequencing library preparation.

Internal barcode validation using short-read sequencing
A volume of 1 μl of 50 μM of combined master forward (5’–TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGNNNNNGGCTATGAGACTGTTCGCTAAT*C*A*C–3’) and reverse (5’–GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGAGACTAAACTGTGCCCACAAA*G*G*G–3’) primers with three consecutive phosphorothioates from the 3’-end, 44 μl of nuclease-free water, and 50 μl of repliQa HiFi ToughMix (Quantabio, catalog number: 95200-500) were added to 5 μl of the released sample. The samples were amplified for 20 cycles using the manufacturer’s protocol then purified using 1× of AMPure XP beads (Beckman Coulter, catalog number: A63881). Samples were eluted from the magnetic beads using 22 μl of 20 mM Tris with 0.05% (v/v) Tween 20. Concentration of the amplified sample was measured using Qubit fluorescence assay (Thermo Fisher, catalog number: Q33231). Ten nanograms from the first PCR amplification were then taken to cycle-limited indexing PCR. Indexing PCR includes 10 μl of an indexing primer set from IDT® for Illumina® DNA/RNA UD Indexes (Illumina, catalog number: 20027213) or Nextera™ DNA CD Indexes (Illumina, catalog number: 20018708) and repliQa HiFi ToughMix as PCR master mix. Indexed samples were cleaned using 1× of AMPure XP beads and then quantified using quantitative PCR (qPCR). Final pooled libraries were then sequenced using an Illumina 2000 (800 pM loading concentration) using a P1 flow cell with 150×2 reads, 50–80% human genome spike-in for nucleotide diversity, and 2% PhiX internal standard.

Internal barcode analysis and performance metric calculation
Retrieval performance was assessed by analyzing internal barcode sequencing data at varying read depths (10^3, 10^4, 10^5, and 10^6 reads) across three independent biological replicates. Sequencing reads were aligned to reference barcode sequences using the Biopython PairwiseAligner package (version 1.85) with stringent parameters (match: +1, mismatch: -20, gap open: -2, gap extend: -2). Alignments exceeding a score threshold of 35 were classified as matches and tabulated for downstream analysis.

For each query, we calculated the area under the receiver operating characteristic curve (AUC) to quantify discrimination performance independent of threshold selection. Complementing this analysis, distributions of read counts for true positive and false positive barcodes were generated and analyzed to visualize the separation between correctly retrieved target barcodes and any incorrectly retrieved non-target barcodes. True positive barcode counts were defined as reads matching the internal barcodes of known target samples present in the retrieved population. Conversely, false positive barcode counts were defined as reads matching the internal barcodes of known non-target samples that were nonetheless detected in the retrieved population.

Synthetic SARS-CoV-2 sequencing
A portion of the released samples were then processed for sequencing SARS-CoV-2 samples using NEBNext® ARTIC SARS-CoV-2 FS Library Prep Kit for Illumina (New England Biolabs, catalog number: E7658) using the VarSkip primers with several modifications. Complementary DNA synthesis was performed using SuperScript™ IV (catalog number: 18091200), 12 μl of released sample, and 1 μl of 50 μM 20-mer oligodeoxythymidine and 50 μM random hexamers for primers. Reverse transcription reactions were incubated at 50°C for 1 h. Finally, amplicons were amplified for 40 cycles.

Resulting libraries following the NEBNext® ARTIC SARS-CoV-2 FS Library Prep protocol were quantified using qPCR and then sequenced on a Nextseq 2000 (750 pM loading concentration) using a P1 flow cell with 150×2 reads and 1–10% PhiX internal standard.

Sequencing reads were first aligned with the SARS-CoV-2 Wuhan sequence (NC_045512.2) using minimap257 (2.24-r1122). Human-readable sequence alignment maps were converted to binary alignment maps using samtools58 (v1.13). Variant calling was performed using LoFreq59 (v2.1.5). Resulting variant calls were then used to demix the SARS-CoV-2 variants for each sample using Freyja42 (v1.5.3).

Encapsulation of clinical SARS-CoV-2 samples
Copy numbers of the SARS-CoV-2 virus were measured upon receipt using probe-based quantitative reverse transcription polymer chain reaction (New England Biolabs; catalog number: M3019; Thermo Fisher; catalog number: A45583), detecting the N1 gene (Integrated DNA Technologies; catalog number: 10006713), and using synthetic SARS-CoV-2 Alpha variant as positive controls for the calibration curve (Twist Bioscience; catalog number: 103907). A sample volume of 1 µL was used for each qRT-PCR reaction.

To encapsulate, 500 µL of each sample was added to individual 1.5 mL tubes, then diluted to 1000 µL using nuclease-free water. A mass of 1 mg of trimethylammonium-functionalized silica microparticles was then added to the solution. After mixing for 5 seconds using a vortex mixer, 10 µL of 50% N-[3-(trimethoxysilyl)propyl]-N,N,N-trimethylammonium chloride in methanol (TMAPS; Alfa Aesar; catalog number: H66414) and 10 µL of tetraethoxysilane (Millipore Sigma; catalog number: 333859) were then added. The resulting mixture was shaken using a BioShake iQ thermal mixer at 1600 rpm for 4 days, then centrifuged at 1000 × g for 30 s to pellet the encapsulated samples. The supernatant was carefully removed, then 1000 µL of 2% (v/v) 2-azido-N-[3-(triethoxysilyl)propyl]acetamide in ethanol was added. The resulting mixture was further mixed for 16 h at room temperature.

The azido-modified encapsulated samples were pelleted and washed twice with N,N-dimethylacetamide (DMAc; Millipore Sigma; catalog number: 185884), then the particles were re-dispersed with 1000 µL DMAc. A mass of 0.5 mg of DBCO-dPEG®₁₂-tetrafluorophenyl ester (Quanta Biodesign; catalog number: 11366) were added to each azide-modified encapsulated sample then the resulting mixture was shaken at 1600 rpm using a BioShake iQ thermal mixer for 1 h at 40°C.

The tetrafluorophenyl-modified encapsulated samples were pelleted using a centrifuge at 1000 × g for 30 s and washed twice with DMAc, then the particles were re-dispersed with 100 µL DMAc. A volume of 900 µL of 500 mM phosphate buffer (Thermo Fisher; catalog number: J60825.AP) was added. Then, a volume of 10 µL of each DNA barcode as amino-modified DNA oligonucleotides 500 µM in nuclease-free water were added to each sample. Three barcodes for each sample were used to add complexity to the library. The table below shows the DNA barcode assigned to each sample Table 1.

After 16 hours of mixing on a thermomixer at room temperature, the barcoded encapsulated samples were pelleted using a centrifuge at 1000 × g for 30 s, washed twice with 1000 µL of hybridization buffer, then finally resuspended with 1000 µl of hybridization buffer. Barcoded encapsulated samples were kept at room temperature.

Clinical SARS-CoV-2 sequencing
Ten µl of unencapsulated and 500,000 microparticles of encapsulated clinical SARS-CoV-2 samples were prepared for sequencing using NEBNext® ARTIC SARS-CoV-2 FS Library Prep Kit for Illumina (New England Biolabs, catalog number: E7658) using the VarSkip primers with several modifications. Encapsulated RNA was released from microparticles with 25 µl of 5:1 buffered oxide etch, and 12 µl was desalted using a 7k MWCO Zeba column (Thermo Fisher; catalog number: 89878). To remove any residual DNA fragments, all samples were first subjected to DNAse treatment (Thermo Fisher; catalog number: 11766051). First-strand complementary DNA synthesis was performed using SuperScript IV, 12 µL of the released sample or unencapsulated sample, and 20-mer oligodeoxythymidine and random for primers. Reverse transcription reactions were incubated at 50°C for 1 h. Finally, amplicons were amplified for 40 cycles.

Following the rest of the NEBNext® ARTIC SARS-CoV-2 FS Library Prep protocol, the resulting libraries were quantified using qPCR and then sequenced on a Nextseq 2000 (800 pM loading concentration) using a P3 flow cell with 150×2 reads and 2–20% PhiX internal standard.

Sequencing reads were aligned using bwa60 (v.0.7.17-r1188). Sequence alignment maps were then converted to binary alignment maps using samtools58 (v1.13). Variant calling, variant filtering, and consensus generation were performed using GATK61 (v4.6.1.0). Duplicates from binary alignment maps were filtered using GATK MarkDuplicates, then variant calling was performed using GATK HaplotypeCaller. Variants were filtered using GATK Variant Filtration. First, variants with a QualByDepth (QD) value less than 2.0 were excluded, using a filter tag QD2. QD provides a normalized variant confidence score by the depth of sample reads supporting a variant. Variants with a raw quality score (QUAL) below 30.0 were discarded, labeled under the QUAL30 filter. Strand Odds Ratio (SOR), a metric that denotes the symmetry of the variant’s presence in both forward and reverse reads, was also considered. Variants with an SOR greater than 3.0 were filtered out and marked with the SOR3 tag. This ensures that the variant is supported by both forward and reverse reads and isn’t an artifact from a potential strand bias. Further, Fisher Strand (FS) values, which indicate strand bias, exceeding 60.0 led to excluding the respective variants, tagged under the FS60 filter. Variants with a Mapping Quality (MQ) less than 40.0, indicative of the overall alignment quality of reads supporting a given variant, were filtered out and designated with the MQ40 tag. Normalization of filtered variant calling files and variant overlap analyses between encapsulated and unencapsulated samples were performed using BCFtools27 (v1.13). True positive (TP) variant counts were directly inferred from the overlapping VCF, while the false positive (FP) and false negative (FN) counts were derived by subtracting TP from the encapsulated and unencapsulated normalized variant calling files, respectively. Precision was computed as the proportion of TP relative to the sum of TP and FP, and recall was derived as the proportion of TP relative to the sum of TP and FN. Finally, SARS-CoV-2 lineages were analyzed using NextClade62 (v2.12.0).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, the authors have transferred their previously developed method for enabling random access in large-scale DNA data storage into a practical system for nucleic acid storage in the real world. They introduce a scalable nucleic acid storage solution that facilitates efficient retrieval of nucleic acid samples stored at room temperature, avoiding the costly and energy-intensive cold-chain infrastructure typically required for genomic sample preservation. By using barcoded and encapsulated samples, the system allows for rapid and precise retrieval of pooled samples through sophisticated query methods, such as numerical ranges, categorical filters, Boolean classifications and their combinations. The authors demonstrate the effectiveness of their system with a pool of SARS-CoV-2 genomic samples, showcasing its potential for global-scale applications, such as pathogen surveillance and rare disease diagnostics, particularly in under-resourced regions.\nI commend the authors for their innovative approach in tackling the pressing challenges associated with nucleic acid storage on a global scale, which holds substantial promise for dramatically reducing the costs of nucleic acid storage and significantly improving the efficiency of sample storage, transportation, and retrieval. I am confident that the practical application of this approach will foster advancements in the accessibility and scalability of nucleic acid management systems. However, upon reviewing the manuscript, I have identified a few points that could benefit from refinement to enhance clarity and depth, thereby further strengthening the manuscript's overall scientific rigor and persuasive power.\n(1) In the “Database construction” section, the encapsulation method used for nucleic acids of varying fragment lengths raises some important questions. Figures S1 and S2 demonstrate that, after the encapsulation process, the majority of the 85-nt barcode fragments and 5-kb genomic RNA are successfully encapsulated into the microcapsules. However, a comparison of these two figures suggests a potential bias, with larger fragments seeming to be more readily encapsulated than shorter ones. This raises the question of whether there is an inherent tendency in the encapsulation process that favors longer nucleic acid fragments. Furthermore, it would be valuable to explore whether the substantial length differences between nucleic acid fragments could impact the sequencing results, particularly in terms of the accuracy and identification of the sequences.\n(2) In the \"Database labeling and querying\" section, the authors mention that each microcapsule is labeled with a total of either 13 or 14 unique 25-nt ssDNA barcodes. Based on my understanding, these barcodes differ only in their sequences and do not have a specific positional order, with each query feature corresponding to one or more barcodes. Given the inherent properties of DNA molecules, non-specific hybridization is often a concern. When executing a query, how can the system ensure precise matching with the target barcode without resulting in non-specific hybridization? This is particularly crucial for expanding the scale of nucleic acid storage, as a larger dataset would necessitate an even greater number of distinct barcodes. The authors could provide more detailed information on how the method prevents such non-specific interactions, as this would strengthen the reliability of large-scale queries and ensure accurate sample retrieval.\n(3) The legend for Figure 2 states that \"Numerical, Boolean, and categorical metadata are converted into a corresponding logical expression comprising AND, OR, NOT operations on several barcodes.\" However, it appears that Figure 2 primarily demonstrates the encoding for flight date (numerical) metadata only. I recommend that the authors include additional panels to illustrate the conversion operations for the other two metadata types-Boolean and categorical. This would provide a more comprehensive understanding of the entire process. Additionally, the figure shows that the date encoding uses a combination of base-3 and base-4 systems. I would appreciate it if the authors could clarify why such a complex system is used for encoding the date. Would it be possible to simplify this by using the same decimal system for both the year and month, which might only require two barcodes instead of a combination of multiple bases? This clarification would help streamline the explanation and potentially simplify the encoding process.\n(4) In the \"Immunological case study\" section, the authors mention that \"the number of distinct barcodes attached per microcapsule was increased from 3 to up to 14.\" However, it remains unclear what the copy number of each barcode per microcapsule is under these conditions. Are the copy numbers of the different barcodes uniform, or do they vary between barcodes? If the copy number of one or more barcodes is too low, could there be a risk of these barcodes not being adequately detected during either the encapsulation or querying process? This issue is important because low copy numbers could potentially lead to misidentification or the failure to retrieve target samples. I suggest the authors address these concerns by providing more information on the distribution of barcode copy numbers and any strategies employed to mitigate the risk of low-copy barcodes impacting query accuracy.\n(5) In Figure 3B (and similar results presented later), the authors demonstrate the use of fluorescence intensity to distinguish between microcapsules with or without a specific feature. However, it is unclear how the fluorescence intensity threshold for classification is selected. How are the thresholds chosen to differentiate the presence or absence of a particular feature? It appears that the threshold values for different probes and fluorophores may not be consistent. Are these thresholds manually selected, or are they derived through a specific algorithmic approach? Clarifying this process would help ensure the reproducibility and objectivity of the results and would be valuable for readers to understand the robustness of the classification process in the context of different probe and fluorophore combinations.\n(6) It appears that the y-axis scale in Figure 3C may be problematic, as it does not seem to represent the data appropriately. Additionally, I suggest that the y-axis does not necessarily need to start from zero. By doing so, the differentiation between different results is somewhat diminished. A more effective approach might be to adjust the y-axis to a more appropriate range, or alternatively, the authors could annotate each bar in the histogram with its corresponding value (e.g., 0.97).\n(7) I am not entirely clear on the authors' rationale for using AUC as a metric to characterize the retrieval results, as this somewhat complicates the accurate interpretation of the retrieval performance. While it is easy to understand that AUC = 0.99 is better than AUC = 0.75, the use of AUC does not directly inform us about how many correct samples were retrieved and how many incorrect samples were retrieved. According to the authors' explanation, an AUC of 1.0 does not necessarily mean that all the correct microcapsules within a sample were retrieved without any incorrect microcapsules being included. In such a case, a small number of incorrect microcapsules could be misidentified as sequence variants, though they do not actually exist, and are only present due to retrieval errors. This raises an important issue when compared to traditional physical retrieval methods, in which samples stored in individual tubes do not involve such erroneous retrieval.\n(8) I do not fully understand the probe selection method for the three retrieval conditions presented in Figure 4. Intuitively, the selection of \"age = 0\" should be the simplest, yet the authors use three distinct barcodes for this condition. Could the authors provide further explanation for the selection of these specific barcodes? Based on their names alone, it is unclear why these particular barcodes were chosen, and additional clarification would be helpful for understanding the reasoning behind this choice. Furthermore, I am curious whether it is possible to arbitrarily select age ranges, such as \"greater than 34\" or \"less than 57,\" in the same way the authors define other age categories.\n(9) In the \"Epidemiological case study\" section, the authors describe the execution of complex queries through multiple rounds of FAS. While this approach is effective in refining the selection, it raises the question of whether this multi-round process introduces significant time and cost overhead. Could the authors provide more insight into how this method impacts the overall efficiency, particularly in large-scale applications? Furthermore, during these sorting rounds, is there a risk of sample loss or degradation? For example, could target microcapsules be accidentally assigned to non-target populations during a sorting round, potentially leading to their loss or unintended inclusion in subsequent steps?\n(10) In the previous paper of DNA data storage by the authors, it was described that non-target microcapsules could be recovered to avoid consuming irrelevant microcapsules during specific retrieval tasks, thus enhancing the efficiency of the process. However, in this manuscript, I did not find a similar description regarding the recovery of non-target microcapsules (I apologize if I have overlooked this). Could the authors clarify whether such a recovery process is possible in the context of the current method? If so, how would it be implemented to ensure that non-target microcapsules are not consumed during the querying process, particularly in large-scale retrieval scenarios?\n(11) In the “SARS-CoV-2 sequencing” section, the authors mention that \"we attribute [the issues] to the low copy numbers of SARS-CoV-2 retrieved from encapsulation resulting in amplicon dropouts.\" While sequencing coverage is acknowledged as a contributing factor, I would also like to inquire about the overall integrity of the SARS-CoV-2 genome. Does the encapsulation and de-encapsulation process affect the integrity of the genome? Specifically, for long-read sequencing methods, such as nanopore sequencing, maintaining the genome's integrity is crucial for accurate and comprehensive analysis. Could the authors provide additional information on whether the process of encapsulation and de-encapsulation has any impact on the genome’s structural integrity, and how it might influence long-read sequencing results?\nOverall, the authors' proposed method demonstrates significant potential for practical applications, particularly in large-scale nucleic acid storage and retrieval systems. However, there are several points that would benefit from more detailed optimization to enhance the robustness and reproducibility of the approach. I believe that addressing the points raised in my review could contribute to further refining the methodology and ensuring its reliability in real-world scenarios. I hope the authors find my comments helpful for improving their work and advancing the utility of their method.\n(Remarks on code availability)\nI sincerely apologize, but I was unable to locate the authors' code, either through the provided DOI or by searching for the authors' names on Zenodo. I would gently request that the authors verify the links they have provided or consider uploading the code to another platform, such as GitHub.", "reviewer_2": "This study presents an innovative and scalable nucleic acid storage and retrieval system that significantly improves the management efficiency of nucleic acid samples through room-temperature preservation and versatile database-like querying capabilities. The research is rigorously designed, supported by comprehensive experimental data, and demonstrates significant application potential, particularly in pathogen surveillance and genomic medicine. The manuscript is logically structured, with detailed figures and tables, representing a high-quality academic paper requiring only minor revisions before publication.\n1. A groundbreaking nucleic acid storage method is proposed, addressing the high costs and inefficiencies of traditional cryogenic storage while offering a feasible technical solution for global pathogen surveillance and biobanking. Nevertheless, are special methods and cost inputs required for the quality control of samples?\n2. Complex query functions (e.g., numerical ranges, categorical filters, and Boolean logic) are achieved through molecular barcoding and fluorescence-activated sorting (FAS), providing a novel approach for large-scale nucleic acid database retrieval. *But will the stability of fluorescence cause problems for the long-term preservation of samples?*\n3. The system demonstrates efficient retrieval from 96 mock SARS-CoV-2 samples and validates high precision (AUC values close to 1.0), with impressive technical performance. Can it be combined with an AI system to achieve automated management?\n4. The research is not only applicable to pathogen surveillance but also extensible to precision medicine, ecological conservation, and other fields, highlighting its broad societal and scientific significance. Has the author made any relevant attempts?\n5. In the Methods section, it is recommended to briefly specify the design parameters of fluorescent probes and signal amplification (e.g., dye selection, probe concentration) to facilitate experimental replication by other researchers.\n6. Additional quantitative analysis of RNA integrity (e.g., RIN values) after clinical sample encapsulation should be included to further validate the effectiveness of room-temperature preservation.\n7. A comparative discussion with existing commercial storage systems (e.g., cost, throughput) would help emphasize the market competitiveness of this technology.\n8. Potential limitations (e.g., scalability of microcapsule production) should be mentioned, along with a brief outlook on future improvements.", "reviewer_3": "As much of the motivation presented in the introduction focuses on sample preservation, it would be important to assess sample preservation and stability in contrast to traditional cold-chains.\nThe authors write on page 3 that “primers and affinity tags are often made with DNA, capitalizing on the innate specificity of DNA hybridization to ensure precision and scalability of retrieval.” However, multiple studies, and widespread experience of molecular biologists indicate that DNA-based hybridization is not as specific as it may appear. The Microsoft/UW and NC State groups for example estimated only a set of ~20,000 20mer sequences could bind to each other specifically, with substantial non-specific cross interactions occurring otherwise.\nPage 9: “The number of reads of each sample showed clear separation between the retrieved and non-retrieved microcapsules (Figure 3b).” This sentence describes sequencing data but the figure displays flow data.\nI understand the value of the AUC/ROC analysis is specifically providing a global assessment of ideal behavior. However, it is parameter that is more ‘derived’ than simpler metrics, and it is therefore harder to assess some basic performance characteristics. It would also be helpful to quantify the % incorrect samples obtained, the number of false positives and negatives, and how easily a computational threshold can be set on the data to maximize trustworthiness of the process. A stronger argument for why the AUC is being used for this application and why it’s the most appropriate for the metrics that matter would be helpful.\nPage 9: the explanation of the barcodes is confusing, i.e. why do those specific barcodes match to those age ranges? X25, x5, and x1 don’t seem to correspond to any of the ages of interest. In general, the use of barcode names in figures makes following the text and results harder than more intuitive naming.\nThe caption for Figure 4 needs much more detail, at least describing what replicates represent, what the measurement types are.\nIt is unclear why such type of physical querying would be needed in real-world scenarios. For example, wouldn’t all samples be sequenced anyways, and then the metadata be searched digitally? Only sequencing queried samples would leave out important control samples or simply be an inefficient approach scientifically.\nA disproportionate amount of the manuscript and figure space are devoted to communicating the analogy of database search. However, the scientific advances are essentially replicated multiple times through multiple figures and should, from a scientific/conceptual perspective, be really combined into one or two figures. Relatedly, the claim of different ‘types’ of classifications is essentially the same across figures 3-5, despite the authors classifying them as distinct groups of Boolean/categorical/numerical. One could argue they are all forms of multiple input-output Boolean logic.\nI can appreciate the advances made, such as the complexity of the queries being greater than previously made. Or the types of classifications achieved. However, my assessment is that these are much less than an order of magnitude improvement over the authors’ own prior work (in the case of complexity), and the nature of the ‘new’ classifications are ultimately all derived classifications based on the presence or absence of hybridization in different combinatorial patterns. So therefore the present work is more so derivative extensions of the authors’ own previously published work rather than a more sizeable advance. I do not make this assessment lightly. Rather, the test I applied was whether a novel fundamental improvement was required to achieve the advance, or whether a novel class of problem was solved. I did not see the presented work rise to that level appropriate for the venue, although this will ultimately be the journal’s prerogative.\nThere are many metrics important for describing the performance and utility of such systems. For example: 1) Distributions and their skew as a function of processing steps; 2) error rates without being obfuscated by use of derived metrics like ROC/AUC; 3) readout/sequencing efficiency and resultant cost compared to just sequencing samples directly or when kept as separate samples in a traditional cold-chain; 4) copy numbers needed to ensure sequencing accuracy and full recovery of all target samples; 5) degradation as a function of each process step, and compared to traditional storage methods.\nThe authors motivate the use for low-resource or remote environments. Can they comment on alternative methods for how samples would be processed without a flow cytometer/sorter which would be expensive and generally need access to regular maintenance technicians and parts.\nIn my opinion, suggestions for advances that would not be considered derivative of the authors’ prior work would include: assessment of performance when scaled beyond 20k distinct barcode sequences (with all of that sequence diversity actually present in the samples), new forms of organization/search that are not derived fundamentally from Boolean logic (i.e. search example using ML from UW/Microsoft would be one example), scaling the complexity of the search beyond what can be done with 3-4 colors and a couple sorts in series, and the ability to make use of less volume/mass of actual patient samples compared to traditional approaches without sacrificing the ability/quality/accuracy of retrieving sequence data or epigenetic data from the samples.\nThe Discussion section paints an impressive picture of potential capabilities, but not one distinct from the potential painted by the authors’ prior work. It should address the specific aspects of the presented work. I.e. it is too general and could be improved in scientific rigor.\nTypos:\nPage 4: such ‘as’ symptomatic/asymptomatic\nPage 12-13: the paragraph had a large duplication of text."}, "author_response": "We thank the reviewer for their positive assessment of our work and for recognizing the potential impact of our approach on global-scale nucleic acid storage and retrieval applications. We appreciate the thoughtful evaluation of our manuscript’s strengths and the constructive feedback provided.\n(1) In the “Database construction” section, the encapsulation method used for nucleic acids of varying fragment lengths raises some important questions. Figures S1 and S2 demonstrate that, after the encapsulation process, the majority of the 85-nt barcode fragments and 5-kb genomic RNA are successfully encapsulated into the microcapsules. However, a comparison of these two figures suggests a potential bias, with larger fragments seeming to be more readily encapsulated than shorter ones. This raises the question of whether there is an inherent tendency in the encapsulation process that favors longer nucleic acid fragments. Furthermore, it would be valuable to explore whether the substantial length differences between nucleic acid fragments could impact the sequencing results, particularly in terms of the accuracy and identification of the sequences.\nWe would like to clarify that the apparent difference in encapsulation efficiency between the 85-nt barcodes and 5-kb SARS-CoV-2 genomic RNA is primarily due to differences in the initial input quantities rather than a length-dependent bias in the encapsulation chemistry. For the internal barcodes, we used 5 nanomoles per sample (approximately 10^14 copies), while for the SARS-CoV-2 genomic RNA, we used only 10^7 copies per sample, per the vendor’s specification. This substantial difference in starting material explains the different patterns observed in Supplementary Figures S1 and S2.\nWe intentionally used a much higher quantity of internal barcodes (5 nanomoles) to ensure that even the smallest contamination during sorting could be detected with high sensitivity in our validation process. This approach allowed us to rigorously assess the specificity of our retrieval method by enabling detection of even minimal cross-contamination between sorted populations. As mentioned in the Main Text, our silica encapsulation chemistry relies on electrostatic interactions between the negatively charged phosphate backbone of nucleic acids and the positively charged silica matrix. This mechanism is inherently length-independent, as the charge-to-mass ratio remains constant along the phosphate backbone regardless of fragment length. In both cases, the cycle threshold values in the supernatant were comparable to or above those of no-template controls, indicating near-quantitative encapsulation for both fragment types despite their length differences and different input quantities. This confirms that our encapsulation process effectively captures nucleic acids regardless of their length. It is notable that in our previous papers where we stored 15 kb plasmid DNA (Banal et al. Nat. Mater. 2021), 2.8 Mb S. aureus genomic DNA, ~1–5 kb H. sapiens total RNA, 5 kb fragments of SARS-CoV-2 RNA, 2.5 Gb M. musculus genomic DNA, and 3.0 Gb B. taurus genomic DNA, we did not observe a size selectivity (Banal & Bathe, ACS Appl. Mater. Interface, 2021). We have revised the Main Text to include these clarifications:\nPAGE 6\nIn contrast to our previous work2,34, where each encapsulation reaction contained a single nucleic acid species such as plasmid DNA2, or various other individual nucleic acids like mammalian and bacterial genomic DNA, total RNA from human cells, or SARS-CoV-2 RNA34, the current work employed co-encapsulation of multiple nucleic acid types within each reaction. Specifically, the nucleic acids introduced into each encapsulation reaction consisted of ~1015 copies of 85-nt ssDNA internal barcodes that uniquely identified each “patient” sample for purposes of retrieval validation and, for designated samples, ~107 copies of 5-kb fragments of synthetic SARS-CoV-2 RNA. Because the encapsulation process relied solely on charge interactions between the negatively charged phosphate backbone of nucleic acids, we expected successful incorporation of all nucleic acids irrespective of length or type (Supplementary Figure 1), as previously demonstrated34. Subsequent validation using qPCR and RT-qPCR confirmed efficient nucleic acid encapsulation and de-encapsulation, yielding an estimated average of approximately 1 copy of SARS-CoV-2 genome and ~108 copies of the internal barcode per microcapsule.\nRegarding the impact on sequencing results, we designed our experimental approach to mitigate any potential length-dependent biases. The internal barcodes and SARS-CoV-2 genomic fragments were sequenced using different PCR amplification protocols optimized for each fragment type, namely standard PCR with 85-nt DNA, and amplicon-tiling for SARS-CoV-2 (ARTIC protocol). The high AUC values (0.9–1.0) consistently achieved across our different queries demonstrated that the retrieval and sequencing accuracy remained robust across both fragment types.\n(2) In the \"Database labeling and querying\" section, the authors mention that each microcapsule is labeled with a total of either 13 or 14 unique 25-nt ssDNA barcodes. Based on my understanding, these barcodes differ only in their sequences and do not have a specific positional order, with each query feature corresponding to one or more barcodes. Given the inherent properties of DNA molecules, non-specific hybridization is often a concern. When executing a query, how can the system ensure precise matching with the target barcode without resulting in non-specific hybridization? This is particularly crucial for expanding the scale of nucleic acid storage, as a larger dataset would necessitate an even greater number of distinct barcodes. The authors could provide more detailed information on how the method prevents such non-specific interactions, as this would strengthen the reliability of large-scale queries and ensure accurate sample retrieval.\nA cornerstone of our strategy to minimize non-specific hybridization from the outset is the use of orthogonal barcodes selected exclusively from the Xu et al. PNAS 2009 library. This library of 240,000 sequences was generated after a rigorous computational design and experimental validation process specifically aimed at creating a large set of orthogonal sequences with minimal potential for cross-hybridization. By starting with barcodes that are intrinsically orthogonal to one another, we significantly reduced the baseline probability of unwanted interactions when many different barcode sequences were present simultaneously in our pooled microcapsule system. This point is now highlighted in the Main Text.\nPAGE 6\nFor each microcapsule, each feature value was encoded into a set of barcodes to be displayed on its exterior (Figure 1a), with a total of either 13 or 14 unique 25-nt ssDNA barcodes per microcapsule. These barcodes were derived from a library of 240,000 orthogonal 25-mers35, which were computationally designed and experimentally validated for orthogonality to ensure minimal cross-hybridization even when many distinct barcode sequences were used within a pooled system.\nOur hybridization protocol employs stringent conditions (10% formamide, 10% dextran sulfate) that destabilize non-specific interactions while preserving perfect-match binding. We have highlighted this now in our Methods section.\nPAGE 22\nSelection of microparticles. An aliquot of 500 µl of the sample library was placed in a 1.5-ml tube and then centrifuged at 1000 rcf to sediment the microparticles. The supernatant was removed then the microparticles were re-dispersed with 200 µl of hybridization buffer (10× SSC, 10% (v/v) dextran sulfate, 10% (v/v) formamide, and 0.05% (v/v) Tween 20). The hybridization buffer was optimized to create stringent conditions that minimized non-specific binding by destabilizing weak or mismatched duplexes. Separately, equivolume of barcode probes, adapters, and fluorescent probes, and 1× SSC were pre-hybridized using the following method: 98°C for 10 seconds, 40°C for 5 minutes, 20°C for 2 minutes. A volume of 20 µl of prehybridized probe solutions were then added to the microparticle suspension. The resulting mixture was shaken at 1500 rpm for 15 minutes at 35°C using a BioShake iQ thermal mixer then centrifuged at 1000 rcf to sediment the microparticles.\nFollowing hybridization, we performed multiple washing steps under controlled ionic strength conditions that further eliminated weakly bound probes.\nPAGE 22\nThe supernatant was removed, and the microparticles were re-dispersed in 1000 µl of the sorting buffer, composed of 1× SSC and 0.05% (v/v) Tween 20, to further remove any non-specifically bound probes. The microparticle sedimentation and washing steps were repeated thrice. The microparticles were finally resuspended in 1000 µl of sorting buffer. The fluorescently labeled microparticles were sorted using a Sony SH800 sorter equipped with a 100-µm sorting chip.\nThe clear bimodal population distributions observed in our fluorescence-activated sorting data (Figures 3B, 4, and 5) provide direct experimental evidence of high discrimination between target and non-target populations. Most importantly, our consistently high AUC values (0.9–1.0) across all queries demonstrate quantitatively that non-specific hybridization did not meaningfully impact retrieval accuracy.\n(3) The legend for Figure 2 states that \"Numerical, Boolean, and categorical metadata are converted into a corresponding logical expression comprising AND, OR, NOT operations on several barcodes.\" However, it appears that Figure 2 primarily demonstrates the encoding for flight date (numerical) metadata only. I recommend that the authors include additional panels to illustrate the conversion operations for the other two metadata types-Boolean and categorical. This would provide a more comprehensive understanding of the entire process. Additionally, the figure shows that the date encoding uses a combination of base-3 and base-4 systems. I would appreciate it if the authors could clarify why such a complex system is used for encoding the date. Would it be possible to simplify this by using the same decimal system for both the year and month, which might only require two barcodes instead of a combination of multiple bases? This clarification would help streamline the explanation and potentially simplify the encoding process.\nWe have modified Figure 2 to include examples of encoding categorical and Boolean metadata, and also adjusted the numerical metadata example to be consistent with our choice of mixed-radix representation elsewhere; in the previous version, we had simplified the representation of the month for demonstrative purposes. We have updated the figure caption and clarified that the mixed-radix representation is used to allow easier representation of more ranged queries. The simplest range queries to perform are those that correspond to specifying a subset of the digits, while multiple ranges of this type can be combined to perform other range queries. We chose to optimize for this feature of the system at the cost of an additional barcode because we have not yet reached the maximum number of possible barcodes per microcapsule. We also refer to the SI, which provides additional detail on the encoding of numerical metadata.\nPAGE 7\nFigure 2 | Conversion of search query to operations on a molecular database. a, Each query is composed of criteria based on numerical, Boolean, and categorical metadata. b, Each metadata feature corresponds to a set of barcodes, with different encoding strategies depending on data type. In this example, the numerical metadata \"date\" is represented by one barcode per digit for a total of seven barcodes (two for the month, three for the day, and two for the last two digits of the year). The year is in base 10 while the month and day are represented using a mixed-radix representation combining bases 3 and 4, with the place value of each digit indicated by subscript. Ranges of contiguous dates are specified by omitting one or more barcodes (see Supplementary Figure 6 for additional details). For the categorical metadata \"city,\" each possible value is represented by a distinct subset of three barcodes. The Boolean metadata \"symptomatic\" is indicated by the presence (True) or absence (False) of a single barcode. c, Any query corresponds to a corresponding logical expression comprising AND, OR, NOT operations on several barcodes. This logical expression then guides the dye-labeling strategy, search grouping, and dispensing of dye-labeled DNA barcodes for sample selection. Selected samples are then retrieved using optical sorting.\n(4) In the \"Immunological case study\" section, the authors mention that \"the number of distinct barcodes attached per microcapsule was increased from 3 to up to 14.\" However, it remains unclear what the copy number of each barcode per microcapsule is under these conditions. Are the copy numbers of the different barcodes uniform, or do they vary between barcodes? If the copy number of one or more barcodes is too low, could there be a risk of these barcodes not being adequately detected during either the encapsulation or querying process? This issue is important because low copy numbers could potentially lead to misidentification or the failure to retrieve target samples. I suggest the authors address these concerns by providing more information on the distribution of barcode copy numbers and any strategies employed to mitigate the risk of low-copy barcodes impacting query accuracy.\nIn our approach, we aim for approximately 10^7 copies per barcode type when using up to 14 distinct barcodes per microcapsule, compared to approximately 10^8 copies per barcode in our previous work with only 3 barcodes (Banal et al., Nat. Mater. 2021). However, we acknowledge that precise quantification of individual barcode copy numbers is challenging due to inherent variability in DNA synthesis quality, concentration measurements, and the conjugation chemistry. While these technical challenges prevented us from directly measuring the distributions of barcode copy numbers, we have confidence in our system because we did not have difficulty distinguishing negative and positive signals in our fluorescence intensity histograms, even without active signal transformation besides standard logarithm conversion for flow cytometry (Figures 3B, 4, and 5), and because we achieved high AUC values (0.9–1.0) across all our retrieval experiments.\nWe believe this strong performance is due to a few aspects of our system that improve signal relative to background noise. First, we use only a single fluorescence threshold for each probe, such that it is only necessary to distinguish the probe signal from background. This is a simpler approach than, for example, classifying samples using multiple fluorescence intensity levels per channel, and reduces the impact of variation in barcode copy number per microcapsule.\nSecond, we developed a fluorescence amplification approach using branched DNA probes (Supplementary Figure 7). Branched probes are often used to detect low copy numbers in cells in flow cytometry or as bright stains for tissue immunofluorescence imaging. Crucially, even with a simple design, this approach already provides 3- to 9-fold signal enhancement. This amplification raises the signal from all barcodes above the detection threshold, creating clear separation between positive and negative populations even for barcodes that might be present at lower copy numbers. Thus, this signal amplification approach mitigates the risk of false negatives even when there is some variability in the barcode copy numbers. Although this approach was effective for the demonstrated system, more advanced signal amplification techniques exist and could be applied for even greater sensitivity if future applications require an even higher multiplexing of barcodes per microcapsule, such as rolling circle amplification (see for example patent WO2025059212A1) or higher-order structured DNA systems displaying combinations of quantum dots and dyes (Chen et al, Nat. Commun. 2022).\n(5) In Figure 3B (and similar results presented later), the authors demonstrate the use of fluorescence intensity to distinguish between microcapsules with or without a specific feature. However, it is unclear how the fluorescence intensity threshold for classification is selected. How are the thresholds chosen to differentiate the presence or absence of a particular feature? It appears that the threshold values for different probes and fluorophores may not be consistent. Are these thresholds manually selected, or are they derived through a specific algorithmic approach? Clarifying this process would help ensure the reproducibility and objectivity of the results and would be valuable for readers to understand the robustness of the classification process in the context of different probe and fluorophore combinations.\nOur fluorescence intensity thresholds for distinguishing between positive and negative populations were determined using a systematic approach to experimental controls. For each sorting experiment, we first established baseline fluorescence distributions using control samples with known barcode presence/absence. These controls helped calibrate the expected signal ranges for each fluorophore. We have added a section on signal calibration for our FAS system\nSupplementary Section 8\nWe performed compensation controls for each fluorophore before every experiment to validate minimal spectral crosstalk between detection channels and to ensure distinguishable fluorescence signals across the multi-color panel. The compensation matrix shows single-stained controls for Atto 488 (detected in the 515_20 Blue B-A channel), Atto 565 (detected in the FL3-A : PE-A channel), Alexa Fluor 647 (detected in the FL4-A : APC-A channel), and Alexa Fluor 750 (detected in the FL6-A : APC-Cy7-A channel), along with unstained controls.\nFor example, we validated that there is no significant fluorescence spillover from Atto 488 into the Atto 565, Alexa Fluor 647, or Alexa Fluor 750 detection channels that would otherwise compromise the ability to distinguish different particle populations in multi-color experiments. Supplementary Figure 14 demonstrates the fluorescence profiles for each single-stained control across all four detection channels, confirming appropriate compensation settings and minimal spectral overlap between fluorophores.\nSupplementary Figure 14 | FAS controls for four-color panel. Each row (a–d) represents a different detection channel: a, Atto 488 channel (515_20 Blue B-A), b, Atto 565 channel (FL3-A : PE-A), c, Alexa Fluor 647 channel (FL4-A : APC-A), and d, Alexa Fluor 750 channel (FL6-A : APC-Cy7-A). Columns show unstained control and single-stained samples for Atto 488, Atto 565, Alexa Fluor 647, and Alexa Fluor 750. Gray shaded histograms indicate fluorescence intensity distributions.\n(6) It appears that the y-axis scale in Figure 3C may be problematic, as it does not seem to represent the data appropriately. Additionally, I suggest that the y-axis does not necessarily need to start from zero. By doing so, the differentiation between different results is somewhat diminished. A more effective approach might be to adjust the y-axis to a more appropriate range, or alternatively, the authors could annotate each bar in the histogram with its corresponding value (e.g., 0.97).\nWe apologize for the mistake in the location of the y-axis ticks, which have been corrected in the revised manuscript. We also agree that the current y-axis scaling does not optimally highlight the differences between the AUC values, which are all high (>0.9) but still show meaningful variation. The standard practice of starting the y-axis at zero indeed diminishes the visual distinction between these high-performance results. Figure 3–5 have been revised to highlight the differences between the AUC values.\n(7) I am not entirely clear on the authors' rationale for using AUC as a metric to characterize the retrieval results, as this somewhat complicates the accurate interpretation of the retrieval performance. While it is easy to understand that AUC = 0.99 is better than AUC = 0.75, the use of AUC does not directly inform us about how many correct samples were retrieved and how many incorrect samples were retrieved. According to the authors' explanation, an AUC of 1.0 does not necessarily mean that all the correct microcapsules within a sample were retrieved without any incorrect microcapsules being included. In such a case, a small number of incorrect microcapsules could be misidentified as sequence variants, though they do not actually exist, and are only present due to retrieval errors. This raises an important issue when compared to traditional physical retrieval methods, in which samples stored in individual tubes do not involve such erroneous retrieval.\nWe selected AUC as our primary metric because it was particularly well-suited for evaluating our molecular retrieval system in a single summary metric. AUC provides a threshold-independent measure of discrimination performance that captures the system's ability to rank true positives higher than false positives over the spectrum of possible thresholds. This is relevant in our case because the number of sequencing reads per sample follows an essentially continuous distribution rather than a binary outcome, and no single threshold can capture the system's relative performance on all samples.\nWhile AUC does not quantify all aspects of system performance, it serves as a useful summary metric with implications for practical use cases. For example, a perfect AUC means that every \"correct\" sample is enriched relative to any \"incorrect\" sample by our query process; in this case, applications requiring higher purity could repeat a round of selection additional times to adjust the proportion of false positive samples to be arbitrarily low, without losing any single true positive. A lower AUC indicates that purity can be increased at the cost of possibly losing one or more true positives (consistent with raising the threshold for a conventional binary classifier).\nWe appreciate the reviewer's point that AUC does not communicate what percent of retrieved samples are true positives. However, we preferred to use the AUC as our primary metric because relying on this percentage would not tell the reader whether all true samples were successfully retrieved (e.g., a selection that retrieved only a single true sample but none of the other true/false samples would appear effective). In addition, this percentage would be expected to vary significantly based on the percent of the total samples that we intended to retrieve. In practice, this percentage was low for all retrievals we tested, indicating that our selection framework is robust. However, the presence of incorrect samples is indeed higher than would be expected with individual tube storage, which is a trade-off for the storage density, scalability, and flexible query capabilities of our system.\nPAGES 9-10\nTo validate correct retrieval, sequencing reads of the 85-nt internal barcodes were matched to the known sequences to identify the samples in the sequencing data. We quantified retrieval performance using several metrics. Receiver operator characteristic (ROC) analysis on the internal barcode read counts was performed to compute the area under the ROC curve (AUC). The AUC value provides a threshold-independent measure reflecting the system's overall ability to rank target samples above non-target samples, with 1.0 indicating every target sample is enriched relative to every non-target sample, and 0.5 indicating performance no better than random chance (i.e., a randomly selected target sample is equally likely to be enriched or not relative to a randomly selected non-target sample). For the “NOT symptomatic” query, where the target (non-symptomatic samples) constituted a high proportion (93 of 96 samples, or ~96.9%) of the initial pool (Figure 3b), the mean AUC over 3 replicates was consistently high, approaching 1.0 with increasing read depth (Figure 3c).\nTo better communicate the presence of false positive samples after selection, we have decided to also report the distributions of mean reads (with standard deviations) over the set of true samples and over the set of false samples (see plots above addressing AUC y-scale concerns). We have modified the main text to include these additional metrics, and to better describe the connection between AUC and enrichment. We have also added a section in the Supplementary Information with all individuals read counts for each sample for each query performed.\n(8) I do not fully understand the probe selection method for the three retrieval conditions presented in Figure 4. Intuitively, the selection of \"age = 0\" should be the simplest, yet the authors use three distinct barcodes for this condition. Could the authors provide further explanation for the selection of these specific barcodes? Based on their names alone, it is unclear why these particular barcodes were chosen, and additional clarification would be helpful for understanding the reasoning behind this choice. Furthermore, I am curious whether it is possible to arbitrarily select age ranges, such as \"greater than 34\" or \"less than 57,\" in the same way the authors define other age categories.\nOur age representation uses a base-5 number system with three positions: a \"25s\" position (multiples of 25), a \"5s\" position (multiples of 5), and a \"1s\" position (multiples of 1). Any age value is decomposed into these three components. For example, age 37 would be represented as 1×25 + 2×5 + 2×1, corresponding to the barcodes bc_age_x25_seq1, bc_age_x5_seq2, and bc_age_x1_seq2. Range queries are performed by omitting one or more barcodes corresponding to less-significant digits. For example, in the previous example, omitting bc_age_x1_seq2 (i.e., only selecting with bc_age_x25_seq1 and bc_age_x5_seq2) would retrieve samples corresponding to the range 35-39. This means that a larger range actually uses fewer barcodes, because it is a less restrictive query. For \"age = 0\", the value is decomposed as 0×25 + 0×5 + 0×1, requiring the presence of bc_age_x25_seq0, bc_age_x5_seq0, and bc_age_x1_seq0. All three barcodes are used for this selection, because we don't wish to include any other values in this range.\nFor other age ranges in Figure 4, we applied the same encoding principles. The range \"15 ≤ age < 20\" corresponds to ages sharing the digits 0 in the 25s position and 3 in the 5s position (since 15 = 0×25 + 3×5 + 0×1), with the 1s digit varying from 0 to 4. Thus, we selected bc_age_x25_seq0 and bc_age_x5_seq3. Similarly, for \"50 ≤ age < 75\", all ages share the digit 2 in the 25s position (since 50 = 2×25 + 0×5 + 0×1), so only bc_age_x25_seq2 was needed. This encoding system also enables flexible range queries, including arbitrary ranges like \"greater than 34\" or \"less than 57,\" but these queries must be performed using combinations of the simpler ranges above. For \"age > 34\", we could retrieve samples with:\n(bc_age_x25_seq1 AND (bc_age_x5_seq2 OR bc_age_x5_seq3 OR bc_age_x5_seq4)) [for ages 35-49]\nOR\nbc_age_x25_seq2 [for ages 50-74]\nOR\nbc_age_x25_seq3 [for ages 75-99]\nFor \"age < 57\", we could use\nbc_age_x25_seq0 [for ages 0-24]\nOR\nbc_age_x25_seq1 [for ages 24-49]\nOR\n(bc_age_x25_seq2 AND bc_age_x5_seq0) [for ages 50-54]\nOR\n(bc_age_x25_seq2 AND bc_age_x5_seq1 AND (bc_age_x1_seq0 OR bc_age_x1_seq1)) [for ages 55-56]\nThese arbitrary ranges require multiple queries combined with OR operations, but they remain entirely feasible within our framework. As we mentioned in response to your point (3) above, the choice of radix to represent a numerical metadata feature determines which ranges are easier to query, and there is a trade-off between the number of barcodes used and the different sizes of these easier ranges. However, all ranges are possible to query regardless of this choice.\nThe specifics of how the ranges are queried is included in Supplementary Figure 6. However, we have also added a brief mention to the Main Text of how composite queries like \"age > 34\" can be performed.\nPAGE 10\n(Figure 4a), “15 ≤ age < 20” (Figure 4e), and “50 ≤ age < 75” (Figure 4i). Range queries of other sizes or positions are also possible by combining ranges using AND, OR, and NOT logic. For the narrowest query “age = 0”, we selected samples labeled with barcodes\n(9) In the \"Epidemiological case study\" section, the authors describe the execution of complex queries through multiple rounds of FAS. While this approach is effective in refining the selection, it raises the question of whether this multi-round process introduces significant time and cost overhead. Could the authors provide more insight into how this method impacts the overall efficiency, particularly in large-scale applications? Furthermore, during these sorting rounds, is there a risk of sample loss or degradation? For example, could target microcapsules be accidentally assigned to non-target populations during a sorting round, potentially leading to their loss or unintended inclusion in subsequent steps?\nThe multiple sorting rounds indeed introduce additional time and handling steps compared to single-round selection, but these overheads should be viewed in the context of the significant advantages gained and the overall scale of the application. The entire three-round process described in our study can be completed in approximately 2 hours, including sample preparation, sorting, and between-round processing of approximately ten million individual microcapsules. Note that the first sorting stage is the most time-consuming, because each successive FAS stage only needs to process the samples that passed the previous stage of sorting. This timeframe is reasonable for archival retrieval applications, where the ability to execute complex queries outweighs time considerations. For example, it is comparable to long-lived digital archives intended for infrequent retrieval, like the Amazon S3 Glacier Deep Archive that can take 12 hours for retrieval. Furthermore, when compared to traditional sample handling methods that would require robotic retrieval and reformatting of individual tubes, our approach would offer dramatic time savings for large-scale applications, as multiple rounds of FAS is still significantly faster than individually retrieving millions of samples sequentially. Although direct comparison is difficult, robotic retrieval modules such as the Hamilton Verso offer retrieval speeds of 1500 tubes/hr but are not compatible with storage below -20C. Other Hamilton models compatible with -80C storage (e.g., SAM HD) have slower retrieval speeds of 100 tubes/hr. Both options are far below what our system offers.\nRegarding cost, the primary expenses associated with multi-round sorting are the fluorescent probes and the instrument time. These costs are largely independent of the scale of the database, making our approach increasingly cost-effective as the database size grows. For example, the same quantity of probes and similar instrument time would be required whether selecting from 100 samples or 1 million samples. When amortized across very large sample collections, the per-sample cost becomes negligible compared to traditional storage methods requiring individual tubes, labels, freezers, and robotic handling systems.\nWe could also implement a hierarchical storage approach where samples are distributed across multiple tubes based on broad categories (e.g., geographical region, collection timeframe), each containing perhaps thousands to tens of thousands of samples rather than millions. This intermediate approach would still offer orders-of-magnitude improvement over the one-sample-one-tube format currently used while reducing the search space for any given query, potentially eliminating the need for multiple sorting rounds in many cases. This flexibility in implementation allows our system to be optimized for different scales and application requirements.\nRegarding sample integrity and retrieval fidelity, the clear population separations in our FAS data (Figures 3B, 4, and 5A), conservative gating strategies, and high AUC approaching 1.0 achieved after three rounds (Figure 5B) provide strong empirical evidence that, at our experimental scale, overall sample loss or misclassification did not compromise the final retrieval accuracy. Nevertheless, we acknowledge that a detailed quantification of microcapsule recovery rates or nuanced molecular effects at each discrete sorting and probe-stripping step, as well as a comprehensive techno-economic analysis for scenarios involving extremely frequent queries on ultra-large-scale databases, were beyond the scope of this proof-of-concept study. Strategies such as hierarchical pooling could also further mitigate overheads by reducing the initial search space for very large archives.\nWe have now added a statement to the Discussion section of our manuscript highlighting these aspects as important areas for future investigation and optimization.\nPAGE 18\nThe successful demonstration herein sets the stage for broader application and further refinement. Future work will seek to characterize per-step efficiencies during multi-round FAS, particularly for very complex queries, and perform comprehensive techno-economic analyses. Such studies will more precisely define throughput capabilities and cost considerations for extremely large archives under high query loads, and enabling quantitative comparisons with freezer farms as implemented by, for example, Azenta Life Sciences. Continued refinement of these operational aspects, potentially incorporating hierarchical storage strategies as well as custom selection and sorting instrumentation, as discussed, may be crucial for facilitating the widespread dissemination and adoption of this genomic banking and querying system.\n(10) In the previous paper of DNA data storage by the authors, it was described that non-target microcapsules could be recovered to avoid consuming irrelevant microcapsules during specific retrieval tasks, thus enhancing the efficiency of the process. However, in this manuscript, I did not find a similar description regarding the recovery of non-target microcapsules (I apologize if I have overlooked this). Could the authors clarify whether such a recovery process is possible in the context of the current method? If so, how would it be implemented to ensure that non-target microcapsules are not consumed during the querying process, particularly in large-scale retrieval scenarios?\nWe thank the reviewer for highlighting the operational advantage of recovering non-target microcapsules to ensure archive integrity, a feature integral to the practical application of any pooled molecular database system using FAS.\nThe reviewer accurately recalls our prior work (Banal et al., Nat. Mater. 2021; Banal & Bathe, ACS Appl. Mater. Interfaces 2021), which together validated various aspects of the use of microcapsules for nucleic acid storage and the FAS-based retrieval process, including recovery of non-target samples. The goal of this manuscript was to introduce the next significant advancement: an expressive molecular query language featuring criteria such as numerical ranges and categorical matching, applied here to retrieve specific RNA genomes. The underlying structure of the system in this manuscript still allows the same ability to recover non-target microcapsules, because the FAS process alone does not damage microcapsules (the subsequent de-encapsulation step is performed only on the selected microcapsules). In a fully operational deployment, the non-target fraction, which for highly specific queries like that in Figure 5 comprises nearly the entirety of the starting pool, would indeed be recovered and re-archived without detailed analytical characterization, and thus would be preserved. We have now explicitly noted in the Discussion section of the revised manuscript this critical feature.\nPAGE 17\nBeyond the immediate benefits of consolidation and advanced search, our approach also incorporates features crucial for robust, long-term archive management and utility. First, as we demonstrated in previous work2,34, the FAS-based retrieval system allows for the recovery of non-target populations of the pooled archive after each query. This feature ensures that the overall sample collection remains intact and available for subsequent, unrelated queries.\n(11) In the “SARS-CoV-2 sequencing” section, the authors mention that \"we attribute [the issues] to the low copy numbers of SARS-CoV-2 retrieved from encapsulation resulting in amplicon dropouts.\" While sequencing coverage is acknowledged as a contributing factor, I would also like to inquire about the overall integrity of the SARS-CoV-2 genome. Does the encapsulation and de-encapsulation process affect the integrity of the genome? Specifically, for long-read sequencing methods, such as nanopore sequencing, maintaining the genome's integrity is crucial for accurate and comprehensive analysis. Could the authors provide additional information on whether the process of encapsulation and de-encapsulation has any impact on the genome’s structural integrity, and how it might influence long-read sequencing results?\nWe have revised the manuscript to provide a more detailed explanation, which is directly supported by our data and established literature.\nPAGE 15\nFurther examination of the data indicated that the precision and recall of variant calling for the encapsulated samples were affected by the sequencing coverage (Figure 6b), which we attribute to the low copy numbers of SARS-CoV-2 retrieved from encapsulation resulting in amplicon dropouts (Supplementary Figure 4 and Supplementary Figure 5). The dropouts observed in the low-copy clinical samples (Supplementary Figure 4 and Supplementary Figure 5) appeared stochastic and were not consistently biased against longer amplicons, a pattern characteristic of low template input effects rather than systematic RNA degradation during processing44–46. Crucially, despite these low-template effects in some clinical samples, overall RNA integrity was sufficient for successful variant identification using the short-read ARTIC protocol for both our synthetic retrieved samples (Figure 6a) and the recalled Omicron sub-lineages from the clinical samples (Supplementary Table 2).\nAs now detailed in the manuscript, the amplicon dropouts—primarily seen as reduced coverage for certain amplicons in the low-copy number clinical samples (Supplementary Figures 4 and 5; initial copy numbers in Supplementary Table 1)—are characteristic of low template input effects rather than systematic RNA degradation attributable to our encapsulation or retrieval process. Several lines of evidence support this conclusion. Firstly, the pattern of dropouts in the clinical samples appeared stochastic across duplicates and was not consistently biased against longer amplicons, whereas systematic RNA degradation would typically manifest as a preferential loss of longer amplicons. Secondly, when synthetic SARS-CoV-2 RNA was successfully retrieved using our query system, subsequent tiling amplicon PCR (ARTIC protocol) yielded the expected distribution of fragment lengths (Supplementary Figure S3), demonstrating that the encapsulation and retrieval process itself is compatible with generating amplicons spanning the genome when sufficient template is present. Thirdly, stochastic amplification and amplicon dropout from low-copy viral templates are well-documented challenges in PCR-based diagnostics and sequencing, particularly for SARS-CoV-2, and are not unique to our storage method. Crucially, as stated in the revised manuscript, despite these low-template effects in some clinical samples, the overall RNA integrity preserved by our system was sufficient for the primary analytical endpoint: successful variant identification using the established short-read ARTIC protocol for both our synthetic retrieved samples (Figure 6A) and, importantly, for recalling the correct Omicron sub-lineages from the encapsulated clinical samples (Supplementary Table S2).\nRegarding long-read sequencing, while ensuring the integrity of very long RNA fragments for platforms like Nanopore is an interesting area for future optimization, our current study was intentionally focused on demonstrating the practical application of our versatile querying system with widely adopted short-read sequencing methodologies standard for pathogen surveillance. The successful validation with these methods confirms the utility of our approach for its intended application.\nOverall, the authors' proposed method demonstrates significant potential for practical applications, particularly in large-scale nucleic acid storage and retrieval systems. However, there are several points that would benefit from more detailed optimization to enhance the robustness and reproducibility of the approach. I believe that addressing the points raised in my review could contribute to further refining the methodology and ensuring its reliability in real-world scenarios. I hope the authors find my comments helpful for improving their work and advancing the utility of their method.\nWe again thank the reviewer for their comprehensive and insightful evaluation of our work.\nI sincerely apologize, but I was unable to locate the authors' code, either through the provided DOI or by searching for the authors' names on Zenodo. I would gently request that the authors verify the links they have provided or consider uploading the code to another platform, such as GitHub.\nWe apologize for our error with the provided Zenodo DOI, which included only a subset of the scripts used for data analysis and visualization. Below are links to a GitHub repository with our scripts (without the accompanying data), as well as an alternative link to a Google Drive folder with both the code and the full datasets. The README.txt in the GitHub repository and the top-level folder of the Google Drive link contains instructions for running the scripts.\nGitHub repository: https://github.com/lcbb/BiosampleSQL\nGoogle Drive link: https://drive.google.com/drive/folders/11fppq-B2Js_Axj2lnTprBGcungNh_gYf\nReviewer 2\nThis study presents an innovative and scalable nucleic acid storage and retrieval system that significantly improves the management efficiency of nucleic acid samples through room-temperature preservation and versatile database-like querying capabilities. The research is rigorously designed, supported by comprehensive experimental data, and demonstrates significant application potential, particularly in pathogen surveillance and genomic medicine. The manuscript is logically structured, with detailed figures and tables, representing a high-quality academic paper requiring only minor revisions before publication.\nWe thank the reviewer for their positive assessment of our work.\n1. A groundbreaking nucleic acid storage method is proposed, addressing the high costs and inefficiencies of traditional cryogenic storage while offering a feasible technical solution for global pathogen surveillance and biobanking. Nevertheless, are special methods and cost inputs required for the quality control of samples?\nWe thank the reviewer for this practical question. Our system does not require prohibitive special methods or cost inputs for quality control (QC). In fact, it is designed to integrate straightforward QC steps and aims to reduce overall long-term QC burdens. As we have now elaborated in the revised Discussion section of our manuscript, we employ a multi-layered QC strategy. This includes standard pre-encapsulation assessments and, critically, leverages the co-encapsulated internal DNA barcodes for both ongoing, low-cost archive integrity monitoring, similar to fixity checks in digital data storage, and for post-retrieval validation of query success. This approach, coupled with the benefits of room-temperature stability, is intended to be more cost-effective and less burdensome than traditional cryogenic QC.\nPAGES 17-18\nSecond, our system permits quality control checks beyond the standard pre-encapsulation assessment of nucleic acid integrity and quantity via the co-encapsulated 85-nt internal DNA barcodes within each sample. These internal barcodes can be periodically interrogated via simple PCR—akin to fixity checks in digital data storage—to confirm sample presence and accessibility within the pooled archive without needing to amplify or sequence the primary encapsulated material. This enables efficient, low-cost monitoring of archive integrity over time. Post-retrieval, these same internal barcodes can validate the success and specificity of each query. When coupled with the room-temperature nucleic acid stability endowed by silica encapsulation34, this comprehensive approach to sample preservation and quality control significantly alleviates the long-term operational burdens and costs associated with traditional cold-chain storage.\n2. Complex query functions (e.g., numerical ranges, categorical filters, and Boolean logic) are achieved through molecular barcoding and fluorescence-activated sorting (FAS), providing a novel approach for large-scale nucleic acid database retrieval. But will the stability of fluorescence cause problems for the long-term preservation of samples?\nWe would like to clarify that our approach separates the sample storage from the fluorescence detection process in a manner that avoids long-term fluorescence stability concerns. The DNA barcodes displayed on our microcapsules are not pre-labeled with fluorescent molecules. Instead, fluorescent probes are added only temporarily during the query process, immediately before FAS. This design choice was made precisely to address the concern raised by the reviewer. When samples are stored long-term, they contain only unlabeled DNA barcodes attached to the microcapsule surface. The fluorescent probes are added shortly before sorting and then completely removed after the sorting process is complete. This approach ensures that no photosensitive fluorescent molecules remain on the samples during storage, eliminating concerns about fluorescence degradation, photobleaching, or potential photochemical damage to the samples.\nThe DNA barcodes themselves exhibit exceptional stability when attached to our silica microcapsules when stored in anhydrous conditions, e.g., mineral oil. In our previous work (Banal et al., ACS Appl. Mater. Interface, 2021), we demonstrated that DNA barcodes on microcapsules remain fully functional after months of accelerated weathering at 70°C and 60% relative humidity, with no detectable degradation in hybridization efficiency or specificity.\n3. The system demonstrates efficient retrieval from 96 mock SARS-CoV-2 samples and validates high precision (AUC values close to 1.0), with impressive technical performance. Can it be combined with an AI system to achieve automated management?\nYes, our nucleic acid storage and retrieval system has potential for integration with AI-based management approaches in future implementations. The digital nature of our barcode encoding and the structured data generated during the fluorescence-activated sorting process would provide suitable inputs for machine learning algorithms to optimize query formulation and retrieval strategies. While such integration presents interesting possibilities for enhanced automation and throughput, this direction lies beyond the scope of our current work, which focused on establishing the core molecular encoding and retrieval capabilities. Future research could explore how AI might enhance sample management workflows, optimize barcode assignment, or automate decision-making during the retrieval process, but would require careful validation to maintain the high precision demonstrated in our current implementation.\n4. The research is not only applicable to pathogen surveillance but also extensible to precision medicine, ecological conservation, and other fields, highlighting its broad societal and scientific significance. Has the author made any relevant attempts?\nWhile our current work focused specifically on demonstrating the core capabilities using SARS-CoV-2 as a model system, we have not yet conducted experimental validation in these other domains. The potential applications in precision medicine, ecological conservation, and other fields represent exciting future directions that could leverage the room-temperature storage and flexible query capabilities demonstrated in our current work.\nOur system's ability to preserve nucleic acids at ambient temperatures while maintaining complex metadata relationships could be particularly valuable for applications like biobanking of rare patient samples, preservation of biodiversity specimens from remote locations, or archiving environmental samples for long-term ecological monitoring. However, each of these applications would require specific optimizations and validation studies that extend beyond the scope of the current manuscript.\nWe previously mentioned these related application areas in the Introduction and Discussion. We have added additional detail to the Discussion for further emphasis.\nPAGE 18\nMoreover, with the ongoing transformation of immunoassays52 and spatial tissue data48 into DNA molecules, we anticipate that the encapsulation and barcoding approach that we have demonstrated here can be used to store and query a comprehensive range of genomic, transcriptomic, and proteomic data. Leveraging these capabilities in future work could generalize the application of our approach from pathogen surveillance to related fields such as personalized medicine and ecological conservation.\n5. In the Methods section, it is recommended to briefly specify the design parameters of fluorescent probes and signal amplification (e.g., dye selection, probe concentration) to facilitate experimental replication by other researchers.\nWe have added brief details about dye selection.\nPAGE 19\nSpecifically, 5’-amino-modified barcodes in Echo 384 polypropylene microplates and internal barcodes in 96-deep well plates were purchased as desalted and delivered in nuclease-free water at 1000 μM and in 1× TE at 500 μM, respectively. Dye-labeled probes were selected for their brightness (quantum yield × molar absorption coefficient at the excitation wavelength for each detection channel) while minimizing fluorescence spillover, with the final selections being Atto 488, Atto 565, Alexa Fluor 647, and Alexa Fluor 750. These dye-labeled probes, along with adapter sequences and branch sequences, were received in 1× TE at concentrations of 100 μM, 33 μM, and 10 μM, respectively.\nDye concentrations, assembly concentrations, and final probe concentrations were already mentioned in the Methods section previously (“Selection of microparticles” subsection).\n6. Additional quantitative analysis of RNA integrity (e.g., RIN values) after clinical sample encapsulation should be included to further validate the effectiveness of room-temperature preservation.\nWe appreciate that direct measurement of RNA integrity after encapsulation would provide additional validation of our room-temperature preservation approach. However, there are technical limitations that prevented us from including conventional RIN analysis in the current study.\nThe primary challenge is that both our initial RNA input quantities and post-recovery yields fall far below the sensitivity thresholds of standard RNA integrity analysis platforms. For our clinical SARS-CoV-2 samples, the starting viral RNA was present at low copy numbers typical of patient specimens (103–106 copies), which is insufficient for reliable RIN determination even before encapsulation. Standard RIN analysis typically requires nanogram quantities of total RNA, far exceeding what is available in our experimental system.\nDespite these limitations, we demonstrated functional RNA integrity through our successful sequencing and variant identification results. The ability to accurately detect SARS-CoV-2 variants from our encapsulated samples provides strong evidence that our system preserves RNA with sufficient integrity for genomic analysis, which is the most relevant metric for pathogen surveillance applications.\n7. A comparative discussion with existing commercial storage systems (e.g., cost, throughput) would help emphasize the market competitiveness of this technology.\nWe agree that such a comparison is important and have indeed included it already.\nOur Discussion section provided a comparative perspective on key drivers of cost and throughput:\nPAGE 17\nConsider the traditional storage of 1 million nucleic acid samples. Storing these samples in –80°C freezers, each accommodating 40,000 samples, would require 25 freezers. In contrast, the microcapsule architecture, capable of retrieval from a pool of 10,000 distinct nucleic acids in just one tube at room temperature as we have previously shown2,34, would only require 100 tubes for storage. An entire tube can be queried in around 15 minutes, assuming a FAS rate of 1000 microcapsules per second and a redundancy of 100 microcapsules per sample.\nWe feel these points appropriately provide a comparative discussion on cost and throughput, highlighting the projected advantages of our technology. A comprehensive techno-economic analysis would be needed in the future to ensure the applicability of the system across different use-cases. We have added this point in our discussion in response to Reviewer 1.\n8. Potential limitations (e.g., scalability of microcapsule production) should be mentioned, along with a brief outlook on future improvements.\nWe agree that discussing limitations and future outlook is important for context. In response to similar suggestions from Reviewer 1, our revised Discussion section now includes a dedicated paragraph outlining key areas for future work and optimization. This section specifically addresses the need to further characterize per-step efficiencies in multi-round FAS, perform comprehensive techno-economic analyses for large-scale and high-demand scenarios, and explore operational refinements such as hierarchical storage. These points directly relate to understanding and enhancing the scalability and practical deployment of the system.\nReviewer 3\nAs much of the motivation presented in the introduction focuses on sample preservation, it would be important to assess sample preservation and stability in contrast to traditional cold-chains.\nOur work builds upon the well-documented stability of nucleic acids, including both DNA (10.1002/anie.201411378) and RNA (10.1002/adhm.201500132), when encapsulated in silica, which has shown exceptional protection against degradation under conditions simulating years of room-temperature storage. While our current manuscript focused primarily on querying capabilities rather than storage longevity, our approach leverages these known stability properties of silica-encapsulated nucleic acids. While the primary focus of the current manuscript is the development and validation of a novel, versatile querying system for these archives, the choice of silica encapsulation was deliberate, leveraging its established benefits for long-term, room-temperature sample preservation.\nThe authors write on page 3 that “primers and affinity tags are often made with DNA, capitalizing on the innate specificity of DNA hybridization to ensure precision and scalability of retrieval.” However, multiple studies, and widespread experience of molecular biologists indicate that DNA-based hybridization is not as specific as it may appear. The Microsoft/UW and NC State groups for example estimated only a set of ~20,000 20mer sequences could bind to each other specifically, with substantial non-specific cross interactions occurring otherwise.\nWhile we fully agree that ensuring high specificity is a fundamental challenge in any system using nucleic acid interactions, the operational principles, primary failure modes, and consequently the practical scalability of sequence orthogonality and error-propagation modes of off-target hybridization differ profoundly between FAS-based physical sample retrieval and the PCR-based information retrieval often employed in DNA data storage. Our system’s specificity lies on the direct, reversible hybridization of fluorescently labeled oligonucleotide probes to complementary 25-mer DNA barcodes displayed on microcapsule exteriors. The fundamental energetic drive for this interaction is the formation of stable Watson-Crick base pairs, where perfect matches achieve a significantly more favorable Gibbs free energy change and higher melting temperature than mismatched duplexes. We exploit this by using optimized stringent hybridization and washing conditions, including formamide and controlled salt concentrations, calibrated near the melting temperature of perfect matches, which thermodynamically destabilize and kinetically promote the dissociation of weakly-bound or mismatched probes, while selectively preserving the intended, high-affinity interactions. Crucially, we exclusively use 25-mer barcodes from the Xu et al. PNAS 2009 library, which was specifically engineered and empirically validated via microarray hybridization for specificity and orthogonality by minimizing inter-sequence similarities, e.g., HSP limits <12bp, and problematic motifs to ensure that a probe for “barcode A” shows minimal stable binding to “barcode B” in a direct competitive binding assay.\nIn stark contrast, the DNA data storage systems referenced by the reviewer typically retrieve information by using specific primer pairs to PCR-amplify target DNA strands from vast, solution-phase pools. PCR is an enzymatic process that exponentially amplifies its template using temperature ramps. The critical distinction is how non-specific events are handled. In PCR, if a primer, even with low affinity or transiently, non-specifically binds (misprimes) to an incorrect DNA template sequence in the early cycles, that initial erroneous binding event leads to the enzymatic synthesis of an incorrect DNA strand. This newly synthesized incorrect strand then itself becomes a perfectly valid and efficient template for amplification in all subsequent PCR cycles. An additional mispriming in the other direction will allow exponential amplification, leading to a substantial population of incorrect products after 20–30 cycles, potentially leading to significant data corruption or masking of the true signal. The estimated limits on primer pair numbers (e.g., the ~14,000-20,000 20-mer primer pair estimate in Organick et al.) are therefore heavily influenced by the stringent necessity to prevent the initial mispriming events that can initiate this runaway amplification of false signals. Our FAS-based readout, however, involves no such enzymatic amplification of the initial barcode-probe recognition event. Instead, each microcapsule is assessed independently, and a binary, threshold-based decision classifies it as “probe-positive” or “probe-negative”. Weak, transient, or low-level non-specific probe binding to an incorrect barcode will result in a fluorescence signal per microcapsule that is quantitatively insufficient to surpass our selection threshold and is thus rejected by the sorter, critically preventing the propagation or amplification of such an erroneous signal. More clearly, from a statistical mechanics perspective, at equilibrium, the fractional occupancy of non-target barcodes sites by the probe is exceedingly low, and the average number of non-specifically bound fluorescent probe molecules per microcapsule is insignificant. This fundamental difference in readout mechanisms and error handling—direct, thresholded hybridization detection without amplification of any undesired binding events in our system, versus PCR-amplified signal detection with inherent amplification of any initial mispriming errors in many DNA storage retrieval schemes—means that the primary constraints and failure modes dictating the usable orthogonal sequence space are different.\nThe practical success of our approach in achieving high specificity under these distinct first principles is empirically demonstrated by the consistently high AUC values (0.9–1.0) and the distributions of sequencing reads from on-target and off-target samples, which affirm that non-specific interactions are effectively minimized and thresholded out in our operational context. We have added this clarification in our Discussion and Conclusion section shown below.\nPAGES 16-17\nThe effectiveness and reliability of such a query language depend on the high specificity of the underlying molecular recognition and retrieval process. Our FAS system of retrieval differs from other DNA-based information systems, such as those using PCR amplification. In PCR-based systems, even rare initial non-specific binding or mispriming events can be exponentially amplified over subsequent PCR cycles, introducing false positives and imposing practical limits on usable primer sequence space. Previous studies have shown the number of usable barcodes for PCR-based retrieval may be as low as 14,000 to avoid off-target binding30. In contrast, our retrieval based on direct hybridization of fluorescent probes allows the use of a large library of 240,000 barcode sequences originally designed for use with microarrays35. This is possible because our FAS approach involves no enzymatic amplification of the initial probe-barcode binding event, weak or transient off-target interactions typically result in fluorescence signals below our detection threshold and are thus rejected without propagation or amplification. Furthermore, whereas additional PCR cycles can exacerbate false positives, additional cycles of FAS can progressively enrich on-target samples, improving the purity of the final retrieved samples.\nIn this paper, we illustrate the utility of our query language and specificity of our FAS-based retrieval with a demonstration on a database of 96 samples with up to 14 barcodes per microcapsule. Our system of barcoded microcapsules allows all 96 samples to be consolidated into a single tube resulting in a hundredfold reduction in storage space while maintaining a broad range of sophisticated search capabilities, including combinations of numerical range, categorical, and Boolean queries. This scalability and efficiency in sample management have direct implications for enhancing biosurveillance strategies as proposed by the Nucleic Acid Observatory50.\nPage 9: “The number of reads of each sample showed clear separation between the retrieved and non-retrieved microcapsules (Figure 3b).” This sentence describes sequencing data but the figure displays flow data.\nWe thank the reviewer for pointing out this discrepancy. The sentence on page 9 referring to Figure 3b has been corrected in the revised manuscript to accurately describe the flow cytometry data shown in Figure 3b.\nI understand the value of the AUC/ROC analysis is specifically providing a global assessment of ideal behavior. However, it is parameter that is more 'derived' than simpler metrics, and it is therefore harder to assess some basic performance characteristics. It would also be helpful to quantify the % incorrect samples obtained, the number of false positives and negatives, and how easily a computational threshold can be set on the data to maximize trustworthiness of the process. A stronger argument for why the AUC is being used for this application and why it's the most appropriate for the metrics that matter would be helpful.\nIn response to this, and similar feedback from Reviewer 1, we have revised the manuscript (page 9 and throughout Figures 3, 4, 5) to not only provide a clearer justification for AUC as our primary, threshold-independent metric for assessing the system's fundamental ability to discriminate and rank target samples but also to include additional data regarding the distribution of sequencing reads from on-target and off-target samples. This new data provides additional information about the fidelity of the probe hybridization and FAS selection process.\nIn general, choosing a computational threshold on the data will depend on the particular application, which dictates the relative cost of false positives and false negatives. The new box-and-whisker plots (e.g., Figures 4d,h,l) show the difference in sequencing reads for on-target and off-target samples; note that a full-scale demonstration beyond our proof-of-concept here would have many samples in both the on-target and off-target classes, providing additional resolution in the distribution of counts. Given this data, an optimal strategy for choosing a threshold is an interesting avenue for further work. Possible methods include applying Otsu's method, which exhaustively searches for a threshold to minimize variance within each class, or fitting a Gaussian mixture model.\nPAGES 9-10\nTo validate correct retrieval, sequencing reads of the 85-nt internal barcodes were matched to the known sequences to identify the samples in the sequencing data. We quantified retrieval performance using several metrics. Receiver operator characteristic (ROC) analysis on the internal barcode read counts was performed to compute the area under the ROC curve (AUC). The AUC value provides a threshold-independent measure reflecting the system's overall ability to rank target samples above non-target samples, with 1.0 indicating every target sample is enriched relative to every non-target sample, and 0.5 indicating performance no better than random chance (i.e., a randomly selected target sample is equally likely to be enriched or not relative to a randomly selected non-target sample). For the “NOT symptomatic” query, where the target (non-symptomatic samples) constituted a high proportion (93 of 96 samples, or ~96.9%) of the initial pool (Figure 3b), the mean AUC over 3 replicates was consistently high, approaching 1.0 with increasing read depth (Figure 3c).\nPage 9: the explanation of the barcodes is confusing, i.e. why do those specific barcodes match to those age ranges? X25, x5, and x1 don’t seem to correspond to any of the ages of interest. In general, the use of barcode names in figures makes following the text and results harder than more intuitive naming.\nWe apologize for the confusion with our naming conventions. The naming convention we used (x25, x5, x1) refers to the place-value weighting in our base-5 number system for age encoding. Each age value is represented as a combination of these three components, where \"x25\" represents multiples of 25, \"x5\" represents multiples of 5, and \"x1\" represents multiples of 1. For example, age 37 would be decomposed as 1×25 + 2×5 + 2×1, corresponding to bc_age_x25_seq1, bc_age_x5_seq2, and bc_age_x1_seq2. For \"age = 0\", all three position values are zero (bc_age_x25_seq0, bc_age_x5_seq0, bc_age_x1_seq0). For \"15 ≤ age < 20\", all ages share the same 25s and 5s positions (bc_age_x25_seq0 and bc_age_x5_seq3), with only the 1s position varying. For \"50 ≤ age < 75\", all ages share the same 25s position (bc_age_x25_seq2).\nWe have added clarification to the Main Text on page 10, and also better explained the barcode naming scheme in the caption of Supplementary Figure 6.\nPAGE 10\nNext, we demonstrated how range queries on patient age could be used to explore if certain age groups were more susceptible to different SARS-CoV-2 variants37. Age metadata was encoded numerically using a base-5 representation of three digits with place values 25, 5, and 1. Our conversion of numerical representations to barcodes is described in more detail in Supplementary Figure 6. We considered three age range queries of different size: “age = 0”\nThe caption for Figure 4 needs much more detail, at least describing what replicates represent, what the measurement types are.\nIn response to Reviewer 1’s comments of simpler metrics, we have now added details also adding detailed descriptions of the replicates and measurement types to the captions of Figures 3–5.\nIt is unclear why such type of physical querying would be needed in real-world scenarios. For example, wouldn't all samples be sequenced anyways, and then the metadata be searched digitally? Only sequencing queried samples would leave out important control samples or simply be an inefficient approach scientifically.\nThe current existence of large cold-storage archives of nucleic acid samples, despite the cost of creating and maintaining such facilities, is strong evidence that sequencing of all samples immediately after collection is unsuitable in many contexts, whether due to logistical, economic, or scientific hurdles. Our approach addresses several critical real-world constraints that contribute to these difficulties. Here are some specific examples of the relevance of storing samples rather than sequencing outright.\nFirst, the scale of sample collection for global pathogen surveillance, biobanking, and population genomics is rapidly outpacing sequencing capacity and budgets. For example, the UK Biobank has collected samples from over 500,000 participants, but only a fraction have been fully sequenced due to cost constraints. Similarly, pathogen surveillance programs collect far more samples than can be feasibly sequenced. Our approach enables the preservation of massive collections of samples at room temperature, along with selective retrieval and sequencing of only the most relevant samples. This advantage is particularly valuable in resource-limited settings where sequencing capacity is severely constrained, thus reducing inequities in access to these types of analyses.\nSecond, sequencing technology continues to evolve rapidly, with new methods offering different advantages for specific applications. Samples collected today may benefit from sequencing technologies that become available years in the future. By physically preserving samples in a retrievable format rather than immediately sequencing them all with current technology, our approach enables future reanalysis with improved methods.\nThird, for many applications, the relevant questions emerge only after initial data collection. For example, when a new SARS-CoV-2 variant emerges, researchers need to query historical samples from specific geographic regions or timeframes. Rather than sequencing all collected samples in anticipation of every possible future question, our approach allows targeted retrieval and analysis of the specific subset needed to answer emerging questions.\nWe have added additional detail to emphasize these factors in the Introduction:\nPAGE 2\nComprehensive nucleic acid analyses rely on both short-read and long-read sequencing platforms to generate the most detailed and accurate genomic data. These platforms, along with gold standard assays like mass spectrometry for analyzing epigenomic and RNA modifications18, can be both time- and cost-prohibitive. As a result, most samples are preserved in cold storage for future analysis rather than being fully processed immediately. For example, the UK Biobank has collected approximately 500,000 patient samples, but genetic characterization has been limited to a large pre-selected set of genetic markers. Portions of the original blood samples were preserved at either -80° C or -180° C to enable future analyses.19,20\nThis long-term storage strategy offers several advantages. It reduces upfront costs, allows researchers to take advantage of future improvements in sequencing technology, and enables sequencing efforts to focus on samples that prove relevant to research questions of interest, which may only become apparent long after sample collection. Yet despite its advantages storage of intact nucleic acid samples is also a costly option. Samples require energy-intensive cold-chain infrastructure for sample preservation and, once sequencing is desired, transport to centralized analysis facilities21–23….\nA disproportionate amount of the manuscript and figure space are devoted to communicating the analogy of database search. However, the scientific advances are essentially replicated multiple times through multiple figures and should, from a scientific/conceptual perspective, be really combined into one or two figures. Relatedly, the claim of different ‘types’ of classifications is essentially the same across figures 3-5, despite the authors classifying them as distinct groups of Boolean/categorical/numerical. One could argue they are all forms of multiple input-output Boolean logic.\nI can appreciate the advances made, such as the complexity of the queries being greater than previously made. Or the types of classifications achieved. However, my assessment is that these are much less than an order of magnitude improvement over the authors’ own prior work (in the case of complexity), and the nature of the ‘new’ classifications are ultimately all derived classifications based on the presence or absence of hybridization in different combinatorial patterns. So therefore the present work is more so derivative extensions of the authors’ own previously published work rather than a more sizeable advance. I do not make this assessment lightly. Rather, the test I applied was whether a novel fundamental improvement was required to achieve the advance, or whether a novel class of problem was solved. I did not see the presented work rise to that level appropriate for the venue, although this will ultimately be the journal’s prerogative.\nWe thank the reviewer for their feedback. While our approach builds upon our previous work in DNA data storage, the current manuscript presents several meaningful advances that collectively represent a significant step forward rather than merely an incremental extension. Although it is true that all queries ultimately resolve to the presence or absence of specific barcodes, the type-aware encoding of metadata into barcodes in this paper enables new search capabilities beyond what is practical with a simple extension of our previous work. The data types we introduce (numerical, categorical, and Boolean) are well-known in computer science, but our encoding of each data type required design choices optimized for the strengths and weaknesses of molecular databases.\nFor example, each additional FAS stage can slow retrieval and introduce additional sample loss and retrieval error, so minimizing the number of required stages for a given query is a key concern. Performing ranged queries using a one-barcode-per-value system -- the most straightforward extension of our previous work -- would require checking n barcodes for a range of n values. With this encoding, the large range query demonstrated in Figure 4 (\"50 <= age <= 74\") would require checking 25 barcodes, or 9 FAS stages using 3 color channels per FAS stage, which is wholly impractical. This limitation in our previous labeling scheme is now discussed in more detail in the Introduction.\nPAGE 4\nDespite their advantages, this and related molecular labeling systems30,32,33 remain severely limited due to their inability to perform arbitrary, complex queries such as routinely performed on modern digital databases using Structured Query Language (SQL). As an example, consider labeling samples with barcodes to indicate patient ages between 0 and 99. Previous barcoding schemes would use one of 100 distinct barcode sequences to indicate each possible age value. A simple numerical range query for samples from patients aged 50 to 74 would require checking for the presence of 25 different barcodes, requiring 9 stages of fluorescent sorting assuming up to three barcodes may be checked at each stage. Considering the time taken and selection error compounded at each sorting stage, this type of barcoding scheme quickly becomes impractical, particularly for complex queries combining criteria on several metadata features. However, such complex retrieval operations are essential for genomics and epidemiological applications in which multiple diagnostic conditions, patient criteria, and/or time periods are sought from very large-scale genomic databases that reach tens to hundreds of millions or even billions of samples or patients.\nIn contrast, our mixed-radix encoding allows us to perform range queries by checking only a small number of barcodes. A range query within a space of n numbers requires checking O(log n) barcodes, and the mixed-radix representation can also be chosen to optimize for particular application-relevant ranges. The base-5 representation of ages in this paper allows three range query sizes (1, 5, and 25), and only a single FAS pass is required for any of them (as in Figure 4). Even at a modest sample loss of 5% per FAS stage, the one-barcode-per-value system loses 37% of on-target samples, relative to only 5% loss with the system in the current study; this reduces sample loss by more than 7-fold, and retrieval time by a factor of 9.\nAn additional advantage is that the encoding in this paper requires fewer distinct barcode sequences than the one-barcode-per-value system (representing ages 0-99 requires 14 barcode sequences instead of 100); this is important given the reviewer's prior comment on the limited number of available orthogonal DNA barcode sequences. Thus, the implementation and feasibility of these ranged searches is a significant advance from our previous DNA data storage demonstration.\nThese innovations enabled queries in the present work that are significantly more complex than were possible or realized in our previous papers. Specifically, in Banal et al. (Nature Materials, 2021), the most complex query was a simple test of two Boolean features (e.g., \"wild AND dog\" in Figure 5) performed over a single FAS stage. Here, our epidemiological case study uses the query \"(symptomatic OR NOT vaccinated) AND flight_city = Chicago AND 6 ≤ arrival_month ≤ 8 AND arrival_year = 2020\", requiring checking eight barcodes over three FAS stages. This query therefore represents a substantial advance, both in its semantic complexity and the difficulty of executing its underlying implementation.\nThe demonstrations were also made possible by additional technical advances, such as increasing from 3 to 13-14 barcodes per microcapsule and implementing a fluorescence-amplifying probe to adjust for the lower copy number of each barcode on the microcapsule surface. Demonstrating successful encapsulation, retrieval, and sequencing of viral genomic material with accurate variant identification validates the practical utility of our approach for global pathogen surveillance.\nWe view Figures 3-5 as showcasing distinct, increasingly complex conceptual aspects of our query system (Boolean queries, numerical range queries, and composite queries, respectively), and for this reason we prefer to highlight them separately. We note that, in an effort to save space, we have already combined our demonstrations of categorical filtering and composite queries into one figure (Figure 5).\nThere are many metrics important for describing the performance and utility of such systems. For example: 1) Distributions and their skew as a function of processing steps; 2) error rates without being obfuscated by use of derived metrics like ROC/AUC; 3) readout/sequencing efficiency and resultant cost compared to just sequencing samples directly or when kept as separate samples in a traditional cold-chain; 4) copy numbers needed to ensure sequencing accuracy and full recovery of all target samples; 5) degradation as a function of each process step, and compared to traditional storage methods.\nThe metrics proposed by the reviewer are indeed important for comprehensive system characterization but represent a fundamentally different focus than our current work. Our manuscript demonstrates a novel nucleic acid database query language that enables complex information retrieval from pooled biological samples - a capability not previously demonstrated. The AUC metric specifically addresses the fundamental question at the core of our innovation: can we selectively retrieve target samples based on sophisticated query criteria? The consistently high AUC values (0.9-1.0) provide strong evidence that our system successfully accomplishes this primary objective.\nThe reviewer's suggested metrics would better suit a study focused on system optimization rather than demonstrating a new capability. For instance, characterizing distribution skew across processing steps would be valuable for refining the protocol, but does not address whether the core molecular query functionality works. Similarly, direct error rates at specific thresholds provide less information than AUC in our context, as they depend on arbitrary threshold choices rather than fundamental discrimination capability. While we would be interested to characterize these metrics in a future study, we view them as distinct from this manuscript's purpose of introducing and validating a conceptual advance in molecular database design and application.\nThe authors motivate the use for low-resource or remote environments. Can they comment on alternative methods for how samples would be processed without a flow cytometer/sorter which would be expensive and generally need access to regular maintenance technicians and parts.\nIn low-resource settings, most global health initiatives operate through centralized reference laboratories that already utilize flow cytometry for applications ranging from HIV monitoring to blood typing. Our approach integrates with this existing infrastructure while eliminating the much more prohibitive cold-chain requirements of traditional biobanking. The major accessibility barrier in global sample management is not the availability of core laboratory equipment, but rather the continuous energy requirements and physical constraints of cryogenic storage, precisely the barriers our room-temperature storage system overcomes.\nWe have added detail to the Discussion for clarification.\nPAGE 16\nThe large-scale, global collection of genomic DNA and RNA promises a plethora of new data relevant to health and security sectors, ranging from pathogen surveillance to personalized medicine. Although centralized laboratories for genomic analysis have enabled sequencing of nucleic acid samples even in low-resource settings, the continuous energy requirements and physical constraints of cold-chain storage and transportation remain major accessibility barriers to the creation of large-scale global nucleic acid repositories,26 indicating the need for scalable, low-cost long-term storage facilities with efficient sample retrieval capabilities.\nIn my opinion, suggestions for advances that would not be considered derivative of the authors' prior work would include: assessment of performance when scaled beyond 20k distinct barcode sequences (with all of that sequence diversity actually present in the samples), new forms of organization/search that are not derived fundamentally from Boolean logic (i.e. search example using ML from UW/Microsoft would be one example), scaling the complexity of the search beyond what can be done with 3-4 colors and a couple sorts in series, and the ability to make use of less volume/mass of actual patient samples compared to traditional approaches without sacrificing the ability/quality/accuracy of retrieving sequence data or epigenetic data from the samples.\nWe agree that these directions would represent substantial innovations beyond our current framework. While our present work focused on establishing a molecular database query language using well-characterized components, we believe the suggestions offered by the reviewer highlight important frontiers for the field.\nThe suggestion of non-Boolean search approaches, particularly machine learning-based methods similar to those explored by the UW/Microsoft group, represents an interesting direction that could transform how molecular information is organized and retrieved. These approaches could potentially enable fuzzy matching, relevance-based retrieval, and other capabilities that mirror advanced digital search systems but in the molecular domain.\nThe reviewer’s point about expanding beyond 3-4 colors and sequential sorting constraints identifies a key technical limitation of our current use of FAS. Addressing this would require fundamental innovations in multiplexed molecular detection, potentially through spectral unmixing, spatial encoding, or novel orthogonal detection modalities beyond conventional fluorescence. However, we note that the structure of our query system applies equally well to non-FAS systems, such as selection via magnetic pulldown, and can be applied to molecular databases based on those paradigms as well. This is now mentioned in the Discussion section.\nFinally, we agree that reducing sample volume/mass requirements while maintaining retrieval fidelity would significantly enhance the practical utility of molecular storage systems, particularly for precious clinical specimens. This would demand innovations in both the encapsulation chemistry and detection sensitivity to operate effectively at substantially reduced copy numbers.\nThe Discussion section paints an impressive picture of potential capabilities, but not one distinct from the potential painted by the authors’ prior work. It should address the specific aspects of the presented work. I.e. it is too general and could be improved in scientific rigor.\nThe discussion has been significantly revised in response to the reviewer comments. We now have additional text regarding future improvements to the barcoding scheme, as well as a deeper comparison of FAS to PCR. We hope the reviewer finds the new text to be satisfactory.\nThe effectiveness and reliability of such a query language depend on the high specificity of the underlying molecular recognition and retrieval process. Our FAS system of retrieval differs from other DNA-based information systems, such as those using PCR amplification. In PCR-based systems, even rare initial non-specific binding or mispriming events can be exponentially amplified over subsequent PCR cycles, introducing false positives and imposing practical limits on usable primer sequence space. Previous studies have shown the number of usable barcodes for PCR-based retrieval may be as low as 14,000 to avoid off-target binding30. In contrast, our retrieval based on direct hybridization of fluorescent probes allows the use of a large library of 240,000 barcode sequences originally designed for use with microarrays35. This is possible because our FAS approach involves no enzymatic amplification of the initial probe-barcode binding event, weak or transient off-target interactions typically result in fluorescence signals below our detection threshold and are thus rejected without propagation or amplification. Furthermore, whereas additional PCR cycles can exacerbate false positives, additional cycles of FAS can progressively enrich on-target samples, improving the purity of the final retrieved samples.\nPAGES 17-18\nBeyond the immediate benefits of consolidation and advanced search, our approach also incorporates features crucial for robust, long-term archive management and utility. First, as we have demonstrated in previous work2,34, the FAS-based retrieval system allows for the recovery of non-target populations of the pooled archive after each query. This feature ensures that the overall sample collection remains intact and available for subsequent, unrelated queries. Second, our system permits quality control checks beyond the standard pre-encapsulation assessment of nucleic acid integrity and quantity via the co-encapsulated 85-nt internal DNA barcodes within each sample. These internal barcodes can be periodically interrogated via simple PCR—akin to fixity checks in digital data storage—to confirm sample presence and accessibility within the pooled archive without needing to amplify or sequence the primary encapsulated material. This enables efficient, low-cost monitoring of archive integrity over time. Post-retrieval, these same internal barcodes can validate the success and specificity of each query. When coupled with the room-temperature nucleic acid stability endowed by silica encapsulation34, this comprehensive approach to sample preservation and quality control significantly alleviates the long-term operational burdens and costs associated with traditional cold-chain storage.\nThe successful demonstration herein sets the stage for broader application and further refinement. Future work will seek to characterize per-step efficiencies during multi-round FAS, particularly for very complex queries, and perform comprehensive techno-economic analysis. Such studies will more precisely define throughput capabilities, understanding the optimum balance between number of samples aggregated per tube against search time, and cost considerations for extremely large archives under high query loads. Continued refinement of these operational aspects, potentially incorporating hierarchical storage strategies, will be crucial for facilitating the widespread adoption of this query system. In addition, we hope to investigate further improvements to this barcoding scheme in future studies. For example, more advanced barcoding schemes could incorporate other data types and search criteria, such as text metadata with substring matching, or the use of non-orthogonal barcode sequence design as demonstrated previously for similarity-based searching51.\nTypos:\nPage 12-13: the paragraph had a large duplication of text.\nWe thank the reviewer for identifying these typos, which are now corrected in the Main Text."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I am really happy that the quality of the manuscript has significantly improved following this round of revision. The authors have made substantial progress in terms of rigor, clarity, and comprehensiveness. I sincerely appreciate the authors' thoughtful responses to my previous comments, most of which I believe have now been satisfactorily addressed.\nI have noted that Reviewer #3 raised a few interesting points which, in my view, have not been fully or convincingly addressed, particularly regarding the scientific advancement and practical significance of this work in comparison with prior studies. I am genuinely curious to see how Reviewer #3 will assess the authors' responses, and whether Reviewer #3 may offer any further insights or perspectives.\n(Remarks on code availability)\nI have reviewed the code provided by the authors and found it to be well-structured and clearly presented, demonstrating a strong sense of logic and organisation. While I have not executed the code on my own system, its clarity and coherence suggest that it is likely to be reproducible without major issues.", "reviewer_2": "The author answered my doubts and now I have no more questions.\n(Remarks on code availability)", "reviewer_3": "The reviewer thanks the authors for their thoughtful responses. I would still suggest including additional metrics of the preexisting data in the supplements, beyond AUC. This is not suggesting new experiments, rather simply providing more detailed or different analyses of the same data so it can be evaluated by readers for different purposes. For example, simple % error rates, distribution skews arising from how much of a biological sample is incorporated into each particle, etc.\n(Remarks on code availability)"}, "author_response": "Response to Reviewers\nReviewer #1\nReviewer #1 (Remarks to the Author):\nI am really happy that the quality of the manuscript has significantly improved following this round of revision. The authors have made substantial progress in terms of rigor, clarity, and comprehensiveness. I sincerely appreciate the authors' thoughtful responses to my previous comments, most of which I believe have now been satisfactorily addressed.\nWe sincerely thank the reviewer for their thoughtful and positive feedback regarding the revised manuscript.\nI have noted that Reviewer #3 raised a few interesting points which, in my view, have not been fully or convincingly addressed, particularly regarding the scientific advancement and practical significance of this work in comparison with prior studies. I am genuinely curious to see how Reviewer #3 will assess the authors' responses, and whether Reviewer #3 may offer any further insights or perspectives.\nWe thank the reviewer for highlighting Reviewer #3's prior comment. We have revised the Abstract and Main Text to clarify the significant advance of this work over prior contributions. In particular, although both prior work and the current study rely on barcode presence/absence on samples, the advance in the current study is a type-aware abstraction mapping of metadata categories to barcode sets, which enables higher-order queries including numerical ranges and categorical filters, which are impractical or nearly impossible to implement using the single-barcode-per-value scheme of prior work. Conceptually, this draws an analogy to programming languages: although all code ultimately compiles to machine code, high-level abstractions like classes and inheritance change what can be expressed practically and reliably. In this study, our simpler abstraction with data types expresses higher-order information by building up from the intrinsically binary information encoded by barcode presence/absence.\nImplementing data types is challenging in molecular systems in large part because it requires attaching many barcodes per sample (13–14 in our study). This scale can be prohibitive with many DNA data management systems: For example, PCR-based systems that depend on short (e.g., 150-nt) synthetic oligos or by primer ligation do not easily support such high-multiplex barcoding. As a result, abstractions of this type have not been obvious next steps for the field. We hope that this work motivates the introduction of more advanced abstractions, as well as the continued improvement of PCR-based and other molecular management systems to support these innovations, either with more barcodes per sample or through other methods.\nPAGE 4\n…However, this and prior molecular labeling systems4,5,34 remain limited to simple Boolean queries and do not effectively perform numerical range searches (e.g., for time or age ranges) or categorical searches that are required for efficient, large-scale biospecimen retrieval and are routinely performed on modern digital databases using Structured Query Language (SQL).\nAs an example, consider labeling samples with barcodes to indicate patient ages between 0 and 99. Previous barcoding schemes would associate each possible age value with one of 100 distinct barcode sequences.2,3 A simple numerical range query for samples from patients aged 50 to 74 would require checking for the presence of 25 different barcodes, requiring 9 stages of fluorescent sorting assuming up to three barcodes may be checked at each stage. Moreover, the queries essential for genomics and epidemiological applications routinely involve selection based on multiple patient criteria, diagnostic conditions, and/or time periods, further compounding the number of sorting stages required. Considering the time taken and selection error compounded at each sorting stage, this type of barcoding scheme quickly becomes impractical for complex queries. This renders these prior molecular labeling systems unsuitable for large-scale genomic databases, which contain tens to hundreds of millions or even billions of samples, and where such queries become essential.\nTo enable biosample database implementations that approach the capabilities of modern digital databases, in the current work we develop a scalable biosample database that permits efficient metadata queries for numerical ranges, such as ranges of dates or ages; categories, such as cities or countries; and previously demonstrated Boolean classifications, such as symptomatic/asymptomatic. Central to our approach is the introduction of a type-aware schema that maps each metadata field to a compact, composable set of barcodes (rather than one barcode per value), enabling these queries via barcode presence readout. In this framework, sets of barcodes efficiently encode values across distinct data types, providing a high-level abstraction over the raw binary information encoded by the presence or absence of each barcode sequence.\nAs further support of the practical significance of this work, the Abstract now also highlights the use of human patient-derived viral genomic samples with this system.\nABSTRACT\nConventional collection, preservation, and retrieval of nucleic acid specimens, particularly unstable RNA, require costly cold-chain infrastructure and rely on inefficient robotic sample handling, hindering downstream analyses. These generate critical bottlenecks for global pathogen surveillance and genomic biobanking efforts, prohibiting large-scale nucleic acid sample collection and analyses that are needed to empower pathogen tracing, as well as rare disease diagnostics1. Here, we introduce a scalable nucleic acid storage system that enables rapid and precise retrieval on pooled nucleic acid samples—stored at room-temperature with minimal physical footprint2,3—using versatile database-like queries on barcoded, encapsulated samples. Queries can incorporate numerical ranges, categorical filters, and combinations thereof, which is a significant advancement beyond previous demonstrations that were limited either to single-sample retrieval4,5 or Boolean classifiers2,3. We evaluate our system on a pool of ninety-six mock SARS-CoV-2 genomic samples identified with theoretical patient data including patient age, geographic location, and diagnostic state, demonstrating it offers rapid, multiplexed nucleic acid sample retrieval in a scalable manner. Finally, we apply the system to storage and sequencing of human patient-derived nucleic acid samples, illustrating its real-world applicability to clinical genomic analysis. By avoiding expensive and cumbersome freezer storage and retrieval systems, our approach in principle scales to millions of samples without loss of fidelity or throughput, thereby supporting the development of large-scale pathogen and genomic repositories in under-resourced or isolated regions of the US and worldwide.\nReviewer #1 (Remarks on code availability):\nI have reviewed the code provided by the authors and found it to be well-structured and clearly presented, demonstrating a strong sense of logic and organisation. While I have not executed the code on my own system, its clarity and coherence suggest that it is likely to be reproducible without major issues.\nWe thank the reviewer for taking the time to examine our code, and for their positive feedback.\nReviewer #2\nReviewer #2 (Remarks to the Author):\nThe author answered my doubts and now I have no more questions.\nWe appreciate the reviewer's positive feedback on our revisions and responses to their comments.\nReviewer #3\nReviewer #3 (Remarks to the Author):\nThe reviewer thanks the authors for their thoughtful responses. I would still suggest including additional metrics of the preexisting data in the supplements, beyond AUC. This is not suggesting new experiments, rather simply providing more detailed or different analyses of the same data so it can be evaluated by readers for different purposes. For example, simple % error rates, distribution skews arising from how much of a biological sample is incorporated into each particle, etc.\nWe thank the reviewer for their feedback on our responses to their comments, and for their valuable suggestion to include additional information to characterize our results. Accordingly, we have now added calculations of precision, recall, accuracy, and specificity for each selection in Supplementary Figure 14. Because the task of choosing an ideal threshold for binary classification remains open, we have computed these at the optimal F score, so that these scores represent a theoretical upper bound on the performance of a thresholding procedure, given the current retrieval data. These data are presented in combination with the box-and-whisker plots (Figures 3-5) and raw barcode counts (Supplementary Figures 9-13) that were added during the previous round of revision.\nSupplementary Figure 14\nSupplementary Figure 14 | Performance metrics for all selection queries at varying read depths. Metrics are based on the true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) at binary classification threshold achieving the optimal F-1 score (harmonic mean of precision and recall) and represent a theoretical upper bound in the performance of a thresholding procedure, given the current retrieval data. Precision is defined as TP/(TP+FP); recall as TP/(TP+FN); accuracy as (TP+TN)/(Total distinct samples); and specificity as TN/(TN+FP). The number of distinct samples is 96 for all queries. Results correspond to the queries: a, \"NOT symptomatic\"; b, \"age = 0\"; c, \"15 ≤ age < 20\"; d, \"50 ≤ age < 75\"; and e, “(symptomatic OR NOT vaccinated) AND flight_city = Chicago AND 6 ≤ arrival_month ≤ 8 AND arrival_year = 2020\"."}]

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