Trends in Sciences

Trends Sci. 202 6 ; 23 (3): 12119

Molecular Identification of Spirogyra Species Using rbc L Markers and Their Chemical Compositions

Nopparut Sitthiwong ¹ , Kaewkanlaya Sotthisawad ¹ ,

Supakorn Arthan ^2,* , Jeeraporn Pekkoh ³ and Kittiya Phinyo ⁴

¹ Program of Biology, Faculty of Science and Technology, Sakon Nakhon Rajabhat University,

Sakon Nakhon 47000, Thailand

² Program of Chemistry, Faculty of Science and Technology, Sakon Nakhon Rajabhat University,

Sakon Nakhon 47000, Thailand

³ Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand

⁴ Office of Research Administration, Chiang Mai University, Chiang Mai 50200, Thailand

( ^* Corresponding author’s e-mail: [email protected])

Received: 30 September 2025, Revised: 8 October 2025, Accepted: 15 October 2025, Published: 20 December 2025

Abstract

Spirogyra are green algae that occur widespread in freshwater habitats across northeastern Thailand, yet its species diversity and chemical characteristics remain poorly understood. This study presents a novel integrated approach combining morphological identification, rbc L-based molecular analysis, and chemical profiling (LC-MS and FTIR) to comprehensively characterize Thai Spirogyra species. Six Spirogyra samples were collected from different water resources in Sakon Nakhon province and identified using both morphological characteristics and ribulose-bisphosphate carboxylase ( rbc L) gene sequences. The sequence data of the rbc L gene was analyzed by using the database of the National Center for Biotechnology Information (NCBI). The results found that 6 Spirogyra samples were able to be identified as 3 Spirogyra submaxima and 1 sample each of S. fluviatilis, S. maxima, and S. chungkingensis . All Spirogyra samples were extracted with ethanol, yielding 6 crude ethanol extracts (SPE1 - SPE6). The chemical compositions were analyzed using Liquid Chromatography-Mass Spectrometry (LC-MS). LC-MS analysis of the 6 crude ethanol extracts revealed a total of 60 compounds, of which thirty were consistently detected across all Spirogyra samples, while the remaining compounds were specific to individual extracts. In addition, the FTIR profiles of SPE1-SPE6 were examined using the ATR-FTIR technique. The FTIR spectra indicated similar functional groups across sample s , with absorption bands attributed to hydroxyl groups, CH₂ and CH₃ stretching vibrations, and carbonyl functionalities. These findings demonstrate that integrating morphological, genetic, and chemical analyses provides a more reliable framework for distinguishing Spirogyra species and reveals locality-specific chemical variation that may be valuable for future biochemical and taxonomic research.

Keywords: Spirogyra , rbc L markers, Phylogenetic, Chemical compound

Introduction

Spirogyra is a significant local aquatic resource in Sakon Nakhon Province, Thailand, commonly found and widely used in local cuisine. Scientifically, Spirogyra are green algae, belong to the Class Zygnematophyceae, and are characterized by unbranched filaments with spiral chloroplasts [1-3].

The number of chloroplasts varies from singular to multiple, depending on the age of the cell and species [4,5]. Spirogyra are widespread and occur in a variety of habitats; there are 537 species, 83 varieties, and 53 forma reported globally [6], with at least 22 species identified in Thailand [4,5].

The identification of Spirogyra is based on the morphological characteristics of vegetative cells and the aggregation patterns of reproductive cells, as well as the process of spore formation [7,8]. It is known that these morphological forms have been found to be variable, especially in laboratory conditions. Some In addition, molecular genetic techniques have been applied by analyzing nucleotide sequences of specific genes to assess genetic relationships. The ribulose-biphosphate carboxylase ( rbc L) gene is frequently employed as a reliable genetic marker, which located in the chloroplast and responsible for carbon dioxide fixation during photosynthesis. These sequences were present in many green algae species, including Spirogyra , and the advantage of using the rbc L gene is the provision of high-quality nucleotide sequences, making it a preferred DNA marker for constructing DNA barcodes [9,10].

In the past, many research efforts have focused on the phytochemical composition of Spirogyra , which is derived from its general metabolic processes. As a result of these metabolic activities, algae like Spirogyra typically produce a variety of phytochemical agents, including fatty alcohols, terpenes, carotenoids, phytol , phenolic compounds, and neutral lipids such as fatty acids and esters [11-14]. These compounds contribute to the biological activities and nutritional contents of Spirogyra , highlighting its potential as a natural source for food products, supplements, and medicinal applications [11,15] .

The study of Spirogyra in Sakon Nakhon province, Thailand, has previously found their distribution in several areas, exhibiting morphological differences in terms of size, shape, and the number of chloroplasts observed. There have also been studies on antibacterial properties, nutritional contents, total phenolic content, and phytochemical composition [11,16,17], but molecular biology research and other essential compounds have not yet been investigated concurrently. This study presents a novel, integrated approach combining morphological, molecular ( rbc L), and chemical analyses to characterize Spirogyra species from multiple freshwater habitats in Sakon Nakhon. This information will contribute to enhancing the value of local resources and serve as a guideline for their future utilization.

Materials and methods

Determine the sampling sites

A survey of Sakon Nakhon province’s water resources revealed the presence of Spirogyra . Then, determined the sampling points geographically ( Table 1 ) and collected Spirogyra samples in the field for laboratory study.

Table 1 Sampling points.

Samples	Location	Geographic Coordinate		Area Characteristics
SP1	Ban Phon Ngam, Phon Ngam Subdistrict, Akat Amnuai District		17°41'21.8"N 103°57'38.4"E	Pond
SP2	Ban Nong Pling, Nong Pling Subdistrict, Nikhom Nam Un District		17°10'26.4"N 103°42'53.1"E	Pond
SP3	Ban Tao Ngoy, Tao Ngoy Subdistrict, Tao Ngoi District		16°59'09.5"N 104°10'05.6"E	Pond
SP4	Ban Tha Sa-ak, Khok Si Subdistrict, Sawang Daen Din District		17°37'14.2"N 103°24'07.4"E	Paddy field
SP5	Ban Khok Khon, Khok Si Subdistrict, Sawang Daen Din District		17°38'24.0"N 103°25'14.5"E	Pond
SP6	Khlong Phuong, Natal Subdistrict, Tao Ngoi District		16°59'45.8"N 104°10'25.1"E	River

Study of Spirogyra morphology

The collected Spirogyra samples assigned as SP1-SP6 according to the sampling site were cleaned, prepared as a wet mount slide, and observed under a light microscope (Nikon Eclipse E200). The morphology of algae was studied, including the width and length of cells, number of chloroplasts, the number of chloroplasts turnover, and characteristics of the septum between cells using books and documents such as Takano et al. [8]; John et al. [18]; Stancheva et al. [19]. The samples were photographed by trinocular microscope (Euromex iScope series).

Study in molecular biology

Samples of Spirogyra collected from each sampling site were washed to remove as many contaminants as possible. Then, the samples were dried by blotting and placed into tea bags. The samples were stored in boxes containing desiccant packets (silica gel), and the boxes were sealed tightly [20]. Spirogyra can be diagnosed using molecular techniques by lysing the cells to extract DNA through grinding with glass beads. DNA was then extracted with Geneaid's Plant Genomic DNA Mini Kit (GP100) (Geneaid Biotech Ltd.) and verified on a 1% agarose gel. DNA fragments were amplified by the polymerase chain reaction (PCR) technique using a Taq DNA Polymerase master mix (Ampliqon brand). The primer targeted decoded genes in the chloroplast rbc L (ribulose-1,5-biphosphate carboxylase) sequence (forward primer RH1, 5’ATGTCACCACAAACAGAAACTAAAGC-3’, and reverse primer 1385R, 5’AATTCAAATTTAATTTCTTTCC-3’) [10]. PCR was performed with an initial 94 °C hold for 2 min, followed by 34 cycles of 94 °C for 30 s, 50 °C for 30 s, and 72 °C for 1 min 30 s, with a final extension at 72 °C for 4 min. PCR product was checked with 1% agarose gel against the GeneRuler 1 kb DNA Ladder (Thermo Scientific). Purify DNA fragments were purified by using DNA Clean & Concentrator (Zymo Research Corporation) and the base sequence analyzed using BioLign version 4.0.6.2 and compared with the base sequence data available in the database (GenBank) of the National Center for Biotechnology Information (NCBI). Phylogenetic analyses were conducted using MEGA version 11. The phylogenetic trees were reconstructed by the Maximum Likelihood (ML) and Neighbor-Joining (NJ) methods based on rbc L sequences, with 1,000 bootstrap replicates to assess the robustness of each node. The Tamura-Nei model was applied as the best-fit nucleotide substitution model.

Preparation of crude extracts

Spirogyra samples were collected from each sampling site and washed to remove contaminants such as dust, sand, and benthos. The samples were dried at 45 °C for 48 h or until completely dried by a hot air oven, ground, and stored in an aluminum foil bag. A Spirogyra sample of 100 g was extracted with ethanol (2×150 mL, 72 h each) using the maceration technique at room temperature. After maceration, the mixtures were filtered through Whatman No. 1 filter paper, and the filtrates concentrated using a rotary evaporator. The extraction yielding all Spirogyra crude ethanol extracts (assigned as SPE1-SPE6) were stored at 4 °C until required for further analysis. Each sample was extracted once; therefore, the results represent qualitative chemical profiles rather than replicated quantitative data.

LC-MS qualitative analysis of Spirogyra

Chromatographic analysis was performed in an Agilent Poroshell 120 EC-C18 column (4.6×150 mm ² , 2.7 μ m) using an Agilent 1290 Infinity LC instrument (Agilent Technologies, CA, USA) in conjunction with an Agilent 6540 series QTOF-MS Mass Spectrometry equipped with an ESI source, a diode-array detector (Agilent Technologies, CA, USA), using both positive and negative ions electrospray ionization. The column temperature was maintained at 35 ° C. The samples were dissolved in 1 mg/mL methanol, filtered with a syringe filter PTFE 0.2 μm, added to the LC-MS vial, and analyzed. The mobile phase consisted of A, 0.1% of formic acid in water, and B, 0.1% of formic acid in acetonitrile. Separation of chemical compositions was conducted under the following conditions: t = 0 min, 5% B; t = 1 min, 5% B; t = 10 min, 17% B; t = 13 min, 17% B; t = 20 min, 100% B; t = 25 min, 100% B. The gradient was allowed to re-equilibrate as follows: t = 27 - 33 min, 5% B. The flow rate was 200 μL/min, with an injection volume of 1 μL [21]. Chemical composition identification was done using Agilent mass Hunter workstation software (Qualitative Analysis, version B.08.00, Agilent) and Personal Compound Database and Library (PCDL). Moreover, the MS data, MS/MS fragmentation profiles, and molecular formulas proposed by the MassHunter were compared with previous literature and databases, such as ScienceDirect and Scifinder ⁿ , with additional searches conducted via Google Scholar, to interpret the chemical compositions of the Spirogyra crude extracts [14,22-24]. Chemical compositions with PCDL scores higher than 80 and a mass error of < ±5 ppm were further selected for m / z verification and MS analysis [25].

FT-IR functional groups analysis

The screening of functional groups of SPE1-SPE6 were identified using Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) with a Bruker INVENIO spectrophotometer (Germany). The IR spectrums were recorded in the mid-infrared area in absorbance mode over a wavelength of 4,000 - 650 cm ⁻¹ .

Results and discussion

Identification of Spirogyra

Spirogyra specimens collected from 6 sampling sites were photographed as shown in Figure 1 . The general shape is a cylindrical cell; the chloroplast is spiral and light green in color. The morphological characteristics in each sample were studied, as presented in Table 2 .

Samples SP1, SP4, and SP5 show similar morphology; vegetative cell widths range from 67 - 83 µm and lengths from 81 - 136 µm, with 8 - 9 chloroplasts per cell and a single spiral turn. These studies were compared to the research of Stancheva et al. [19], who studied Spirogyra species in California streams and found that Spirogyra submaxima Transeau exhibits a cell width of 95 - 105 µm, cell length of 100 - 400 µm, and 8 - 9 chloroplasts per cell as well. Sherwood et al. [26] examined the diversity of green algae in the genus Spirogyra in the Hawaiian Islands. The study found that Spirogyra submaximus measures 78.7 - 121.5 µm, with an average of 7.4 chloroplasts per cell and 0.8 spirals. Notably, SP5 showed slightly shorter cells than SP1 and SP4, which may indicate intraspecific variability influenced by environmental factors [27].

Figure 1 Micrograph of Spirogyra SP1-SP6.

Table 2 Morphological of 6 Spirogyra specimen.

Characteristics	SP1	SP2	SP3	SP4	SP5	SP6
Vegetative cell width (µm)	67 - 70	30 - 37	98 - 106	77 - 83	80 - 83	23 - 27
Vegetative cell length (µm)	97 - 110	125 - 140	169 - 260	113 - 136	81 - 117	193 - 206
End wall	Plane	Plane	Plane	Plane	Plane	Plane
Chloroplast per cell	8 - 9	3	4 - 5	8	8-9	3 - 4
Turns per cell	1	2 - 2.5	1 - 1.5	1	1	1 - 1.5
Species	Spirogyra submaxima	Spirogyra fluviatilis	Spirogyra maxima	Spirogyra submaxima	Spirogyra submaxima	Spirogyra chungkingensis

Note: Displayed as minimum-maximum values from 10 replicate samples.

SP2 demonstrated a narrow cell width (30 - 37 µm) and longer cell length (125 - 140 µm), with fewer chloroplasts (3 per cell) and 2 to 2.5 spiral turns per cell. These measurements correspond with the morphology reported for Spirogyra fluviatilis by John et al . [18] and El-Sheekh et al . [28], which showed vegetative cells 30 - 45 µm wide and 70 - 240 µm long, with 3 - 5 chloroplasts and 1.5 - 3.5 turns per cell. The size of the algae found in this study is smaller than that reported in the research by Stancheva et al. [19], which found cells with a maximum width of 45 µm and a maximum length of 240 µm. In the study by Phinyo et al . [29] this species of algae found in Chiang Rai province had a width of 50 - 58 µm and a length of 220 - 250 µm .

SP3 showed the largest cell dimensions, with widths of 98 - 106 µm and lengths ranging from 169 to 260 µm, containing 4 - 5 chloroplasts and 1 - 1.5 turns per cell. This morphology aligns well with Spirogyra maxima . This morphological feature closely resembles that of Spirogyra maxima . Research by Stancheva et al. [19] ; Sherwood et al . [26]; John et al. [18] found that the cells were 118 - 150 µm wide and 90 - 280 µm long, with 5 - 8 chloroplasts per cell, each making 1.5 to 3 turns. And SP6 presented the narrowest cell width (23 - 27 µm) and the longest cell length (193 - 206 µm), with 3 - 4 chloroplasts and 1 to 1.5 spiral turns. These features are consistent with Spirogyra chungkingensis and similar to the research results of Takano et al . [8], which collected Spirogyra from pond or paddy fields in Japan and found Spirogyra chungkingensis had 24 - 27 µm width, 150 - 300 µm length, 3 chloroplasts, and 5 turns per cell.

These morphological observations support previous classifications, confirming the morphological diversity within the genus Spirogyra by using cell size, chloroplast number, and chloroplast turns as taxonomic markers. These characteristics result from responses to the environment, which are related to the physical and chemical factors of the water source [30], and are similar to Hainz et al. [31] research, which found that nutrients are an important factor that causes the formation of morphotype or filament-type groups. In environments with high nutrient levels, the width of the filament increases. Other factors, such as ions, buffering capacity, light intensity, and water temperature, do not have a significant effect on the formation of different shapes. Contrary to the research findings of Berry and Lembi [7], they found that morphological characteristics vary as a result of responses to light and temperature. Under conditions of high light and temperature, which increase net photosynthesis, Spirogyra was observed to grow longer, exceeding 100 µm . Because environmental factors affect morphology variation, species identification based on morphology may be confused. Additionally, specimens correctly identified at the time of collection may later exhibit characteristics similar to other species [10]. The features of the reproductive process are also important for species identification, including the type of conjugation and characteristics of the mature zygospore wall, such as zygospore ornamentation, shape, and color [3,19,31]. However, vegetative filaments of Spirogyra can occur throughout the year, but reproductive filaments are rather rare in the field [19]; only 10% of Spirogyra in natural water resources are found in the sexual reproductive stage [31]. On the other hand, stress conditions such as temperature, drought, and pH induce the formation of conjugation tubes of Spirogyra genera [27,28], but difficulty in inducing conjugation in the laboratory and the mutation rate can occur once per generation [10]. Therefore, many studies have used filaments collected from native habitats [28], alongside molecular techniques which are effective for both accurately identifying and analyzing genetic variation.

In molecular identification of Spirogyra , the chloroplast rbc L gene is widely recognized as a reliable and universal genetic marker, providing high-quality nucleotide sequences suitable for species-level classification. In this study, the rbc L gene was successfully amplified using specific primers, and the resulting DNA fragments were analyzed by PCR. The obtained nucleotide sequences were compared with the NCBI GenBank database, and species identification was further confirmed by constructing phylogenetic trees using the Maximum Likelihood (ML) and Neighbor-Joining (NJ) methods, in conjunction with morphological observations.

The BLAST sequence alignment revealed that isolates SP1, SP4, and SP5 shared high identity percentages with Spirogyra submaxima strain RSS021 (99.23%, 98.64%, and 99.38%, respectively). Isolate SP2 showed the highest similarity to S. fluviatilis , while SP3 was closely related to S. maxima (98.46%) and SP6 to S. chungkingensis (99.84%). These results indicate a high degree of genetic relatedness between the studied isolates and previously characterized Spirogyra species.

The phylogenetic relationships among the isolates were further analyzed based on the rbc L sequences ( Figures 2 and 3 ). Both the ML and NJ analyses consistently separated the isolates into 4 distinct clades corresponding to reference Spirogyra species, reflecting considerable genetic diversity among the samples. In the ML tree, Clade I contained isolate SP2, which clustered with S. fluviatilis but exhibited low bootstrap support (51%), indicating weak confidence in this relationship. Clade II included isolate SP6 grouped with S. chungkingensis (65%), while Clade III comprised isolate SP3 associated with S. maxima (approximately 60% - 70%). These moderate bootstrap values suggest partial but not conclusive support for species-level assignment. In contrast, Clade IV, composed of isolates SP1, SP4, and SP5, displayed high bootstrap support (98%), confirming a strong genetic affinity with S. submaxima.

The NJ phylogenetic tree showed a topology consistent with the ML tree but with slightly higher bootstrap values (98% - 99%), indicating stable clustering patterns across analytical methods. The consistency between the ML and NJ topologies reinforces the robustness of the inferred evolutionary relationships, while the variation in bootstrap values highlights methodological influences and potential limitations of single-gene analyses.

High bootstrap support, such as that observed for Clade IV, is generally considered strong evidence for reliable evolutionary relationships and provides confidence in the identification of isolates SP1, SP4, and SP5 as S. submaxima. Conversely, the weak support in Clade I suggests uncertainty in the placement of SP2 with S. fluviatilis , possibly due to limited sequence divergence, intra-species variability, or insufficient phylogenetic signal within the rbc L gene. Similar findings have been reported in previous studies, where rbc L-based phylogenies provided valuable taxonomic information but limited resolution among closely related or morphologically similar Spirogyra taxa [19].

The differences in bootstrap values between the ML and NJ methods further demonstrate the influence of model assumptions and computational algorithms on phylogenetic inference. Given that rbc L is a relatively conserved chloroplast gene, it may not capture sufficient nucleotide variability to resolve recent divergence events within Spirogyra . To enhance the phylogenetic resolution, future studies should combine rbc L with additional molecular markers, such as nuclear 18S rDNA or ITS-2 regions, providing a more comprehensive framework for taxonomic clarification.

Overall, the results confirm that rbc L is a suitable and reliable marker for preliminary molecular identification and for delineating major Spirogyra lineages. The strong bootstrap-supported clades validate its use in genus-level and some species-level identification. However, weakly supported nodes emphasize the need for a multi-locus approach to achieve a clearer understanding of the evolutionary relationships and species boundaries within Spirogyra .

Figure 2 Phylogenetic tree by Maximum Likelihood method at 1,000 bootstraps (100%) of Spirogyra samples in rbc L gene locus (Only values > 50% shown).

Figure 3 Phylogenetic tree by neighbor-joining method at bootstrap 1,000 times (100%) of Spirogyra samples in rbc L gene locus (Only values > 50% shown).

Qualitative profiling of Spirogyra samples SP1-SP6 using LC-MS method

Six Spirogyra samples were successfully extracted with ethanol yielding crude ethanol extracts SPE1-SPE6. Six Spirogyra samples were initially screened and their chemical compositions determined using LC-MS. The base peak chromatograms of SPE1-SPE6 were obtained as shown in Figure 4 . The data for the identified compounds in positive and negative mode, such as the retention time (RT), the chemical formular, the molecular mass, the mass-to-charge ratio ( m / z ), and the mass error < ±5 ppm, are summarized in Tables 3 and 4 . All the compounds in the 6 Spirogyra crude extracts were identified using the Personal Compound Database and Library (PCDL). The experimental molecular formula correctly matched the quasi-molecular ions, theoretical molecular ions, and fragment ions, as shown by the mass error for molecular ions in all detected compounds being within < ±5 ppm.

The qualitative identification of the chemical composition of all Spirogyra crude ethanol extracts found 54 and 45 compounds in positive and negative modes, respectively. Among the 6 Spirogyra extracts (SPE1-SPE6), nine compounds in positive ionization mode ( Figure 5 ) and 21 compounds in negative ionization mode ( Figure 6 ) were consistently detected across all samples. These consistently present compounds may represent potential biomarkers of Spirogyra species, reflecting shared chemical traits. However, further quantitative and functional studies are required to confirm their biomarker status. The compounds that were commonly identified in all 6 samples serve as potential chemical markers, indicating their specificity to Spirogyra found in Sakon Nakhon, Thailand. Distinct metabolites were detected across SPE1-SPE6. Hexanedioic acid bis(2-ethylhexyl) ester and 1-octadecanol were exclusively detected in SPE1. In SPE2, 1-octadecanol, 4-ethenyl-2-methoxyphenol, 8-hexadecyne, 2,4,4-trimethyl-3-(3-oxo-1-butenyl)-2-cyclohexen-1-one, 9,12-octadecadienoic acid methyl ester, and ( Z , Z )-9,12-octadecadienoic acid ethyl ester were identified, whereas oleic acid and propanoic acid anhydride were exclusively detected in SP3. SP5 contained 4-hydroxy-4-methyl-2-pentanone, hexadecane, and 4,8,12,16-tetramethylheptadecan-4-olide, while (3 β )-stigmasta-5,24(28)-dien-3-ol and cyclotetracosane were uniquely found in SPE6. Notably, SPE4 exhibited the highest diversity, with eight exclusive metabolites: 4-methyl-2-heptanone, decane, ( Z )-3-heptadecen-5-yne, 4-ethenyl-2-methoxyphenol, ( Z )-octa-9-decenamide, (2 E ,7 R ,11 R )-3,7,11,15-tetramethyl-2-hexadecen-1-ol, 4,8,12,16-tetramethylheptadecan-4-olide, and hexanedioic acid bis(2-ethylhexyl) ester. The findings suggest that Spirogyra collected from different locations exhibit broadly similar overall chemical compositions, yet notable variations exist in specific constituent compounds.

In addition, loliolide and hexadecanoic acid methyl ester were not only found in SPE1-SPE6 but also in Spirogyra porticalis [23]. 6,10,14-Trimethyl-2-pentadecanone and linoleic acid were found from Spirogyra neglecta [11,32]. Spirogyra rhizoides and SPE1-SPE6 linolenic acid, linoleic acid, and 9-cctadecenoic acid were observed [33]. Variations in specific constituent compounds are strongly influenced by environmental and nutritional factors, such as light intensity and duration, CO ₂ concentration, pH, temperature, nutrient availability, and biotic interactions [34]. Nutrient input emerged as the dominant factor, influencing the production of all 6 chemical classes (phenolics, pigments, polysaccharides, sterols, diterpenes, and fatty acids). In contrast, hydrodynamics and salinity had a smaller overall impact, primarily affecting pigments (hydrodynamics) and phenolic compounds (hydrodynamics and salinity) [35]. Furthermore, allelopathic effects from SPE1-SPE6 involve the release of distinct secondary metabolites into the environment, thereby influencing the growth, survival, and reproduction of other organisms [36].

Variation in chemical composition is responsible for many biological activities. Spirogyra green algae showed notable in vivo anti-leishmanial efficacy in BALB/c mice [37]. The anti-inflammatory activity of Spirogyra neglecta is mediated through the inhibition of nitric oxide (NO) production [11]. Ethanol crude extracts of 6 Spirogyra spp. exhibited antimicrobial activity against Candida sp., Staphylococcus aureus , and Escherichia coli [17]. Eleven green algae species from Sindh (Pakistan) exhibited significant phytotoxic activity but showed non-significant cytotoxic, insecticidal, and antitumor activities [38]. Moreover, Spirogyra aequinoctialis , Spirogyra pratensis and Spirogyra subsalsa exhibited significant phytotoxic activity against Lemna minor [39].

The LC-MS analysis in this study provides a qualitative overview of metabolite diversity among Spirogyra samples. Many identified compounds are common fatty acids and esters; therefore, the focus is on comparing shared versus sample-specific metabolites rather than assigning unique biomarkers. Variation among samples may reflect ecological differences, but detailed correlations will require further quantitative and environmental investigations.

Figure 4 Positive and negative modes chromatograms of SPE1-SPE6 by LC-MS.

Table 3 Identified compounds from SPE1-SPE6 by LC-MS (positive mode).

No	RT (min) ¹	Mass	m / z (Expected) ²	Chemical Formular	Error (ppm) ³	Identification	Samples (SPE)
No	RT (min) ¹	Mass	m / z (Expected) ²	Chemical Formular	Error (ppm) ³	Identification	1	2	3	4	5	6
1	15.312	196.1096	197.1169	C ₁₁ H ₁₆ O ₃	− 1.79	Loliolide	✓	✓	✓	✓	✓	✓
2	16.477	374.1520	392.1874	C ₂₄ H ₂₂ O ₄	0.60	Phthalic acid, di(3,4-dimethylphenyl) ester	✓	✓				✓
3	17.094	206.1517	229.1411	C ₁₀ H ₂₂ O ₄	− 0.71	Oleic acid			✓
4	17.357	116.0832	139.0724	C ₆ H ₁₂ O ₂	− 4.59	4-hydroxy-4-methyl-2-Pentanone				✓	✓
5	17.525	130.0636	153.0528	C ₆ H ₁₀ O ₃	4.40	Propanoic acid anhydride			✓
6	17.714	128.1210	151.1094	C ₈ H ₁₆ O ₆	0.27	4-methyl-2-Heptanone				✓
7	19.057	256.2398	274.2736	C ₁₆ H ₃₂ O ₂	− 1.82	Tetradecanoic acid, 12-methyl-, methyl ester	✓	✓		✓		✓
8	19.091	212.2133	230.2472	C ₁₄ H ₂₈ O	− 3.14	Tetradecanal		✓		✓		✓
9	19.366	182.2042	200.2368	C ₁₃ H ₂₆	4.24	2,5-dimethyl-2-Undecene	✓
10	19.408	208.1467	209.1540	C ₁₃ H ₂₀ O ₂	1.97	4-(2,2,6-trimethyl-7-oxabicyclo [4.1.0] hept-1-yl)-3-Buten-2-one	✓	✓	✓	✓	✓	✓
11	19.435	180.1153	181.1226	C ₁₁ H ₁₆ O ₂	1.42	Dihydroactinidiolide	✓	✓	✓	✓		✓
12	19.475	270.2546	288.2884	C ₁₇ H ₃₄ O ₂	− 4.82	Hexadecanoic acid, methyl ester		✓				✓
13	19.553	286.2498	304.2835	C ₁₇ H ₃₄ O ₃	− 3.50	Hexadecanoic acid, 2-hydroxy-, methyl ester		✓			✓	✓
14	19.598	142.1728	165.1620	C ₁₀ H ₂₂	4.48	Decane				✓
15	19.623	210.2347	228.2680	C ₁₅ H ₃₀	− 0.40	1-Pentadecene		✓	✓	✓		✓
16	19.758	270.2932	293.2826	C ₁₈ H ₃₈ O	3.48	1-Octadecanol		✓
17	19.533	196.2186	214.2524	C ₁₄ H ₂₈	− 2.74	Tetradecene	✓	✓			✓	✓
18	20.118	232.1827	233.1900	C ₁₆ H ₂₄ O	− 0.07	1-Hexadecanol	✓					✓
19	20.320	298.2865	316.3204	C ₁₉ H ₃₈ O ₂	− 2.33	Octadecanoic acid, methyl ester			✓	✓	✓	✓
20	20.416	184.2197	207.2090	C ₁₃ H ₂₈	3.18	Tridecane						✓
21	20.626	228.2099	251.1991	C ₁₄ H ₂₈ O ₂	4.11	Tetradecanoic acid (Myristic acid)		✓			✓
22	20.650	242.2241	265.2134	C ₁₅ H ₃₀ O ₂	− 1.78	Pentadecanoic acid (Pentadecylic acid)	✓		✓	✓	✓	✓
23	20.892	226.2661	249.2556	C ₁₆ H ₃₁	0.43	Hexadecane					✓
24	20.904	220.1826	221.1896	C ₁₅ H ₂₄ O	− 0.34	2,6-bis(1,1-dimethylethyl)-4-methylPhenol	✓	✓	✓	✓		✓
25	21.023	150.0677	151.0750	C ₉ H ₁₀ O ₂	− 2.80	4-ethenyl-2-methoxyPhenol		✓
26	21.140	266.2970	284.3308	C ₁₉ H ₃₈	− 1.49	1-Nonadecene	✓	✓	✓	✓	✓	✓
27	21.267	324.3027	342.3369	C ₂₁ H ₄₀ O ₂	− 0.45	4,8,12,16-Tetramethyl-heptadecan-4-olide					✓
28	21.374	222.2353	245.2246	C ₁₆ H ₃₀	2.54	8-Hexadecyne		✓
29	21.408	330.2783	331.2857	C ₁₉ H ₃₈ O ₄	3.81	Hexadecanoic acid, 2-hydroxy-1-(hydroxymethyl) ethyl ester	✓	✓	✓	✓	✓	✓
30	21.497	278.2251	279.2323	C ₁₈ H ₃₀ O ₂	1.82	α -Linolenic acid	✓		✓		✓
31	21.549	206.1304	207.1376	C ₁₃ H ₁₈ O ₂	− 1.29	2,4,4-trimethyl-3-(3-oxo-1-butenyl)-2-Cyclohexen-1-one		✓
32	21.575	212.2501	235.2393	C ₁₅ H ₃₂	− 1.43	Pentadecane		✓		✓	✓	✓
33	21.701	280.2411	281.2467	C ₁₈ H ₃₂ O ₂	2.95	Linoleic acid	✓		✓	✓	✓	✓
34	21.749	264.2084	265.2153	C ₁₇ H ₂₈ O ₂	− 1.92	7,10,13-Hexadecatrienoic acid, methyl ester	✓	✓	✓		✓	✓
35	22.014	312.3030	313.3106	C ₂₀ H ₄₀ O ₂	0.51	Arachidic acid			✓			✓
36	22.240	234.2343	235.2419	C ₁₇ H ₃₀	− 1.77	( Z )-3-Heptadecen-5-yne				✓
37	22.527	266.1678	267.1750	C ₁₉ H ₂₂ O	2.62	1,3-diphenyl-4,4-dimethyl-1-Penten-3-ol	✓	✓	✓	✓	✓	✓
38	22.617	268.2398	291.2303	C ₁₇ H ₃₂ O ₂	− 1.67	( Z )-9-Hexadecenoic acid, methyl ester	✓	✓	✓		✓	✓
39	22.711	390.2779	413.2670	C ₂₄ H ₃₈ O ₄	2.31	( Z )-9-Hexadecenoic acid, methyl ester		✓	✓	✓	✓	✓
40	23.063	284.2719	285.2791	C ₁₈ H ₃₆ O ₂	1.41	Heptadecanoic acid, methyl ester	✓	✓	✓	✓	✓	✓
41	23.052	292.2402	315.2292	C ₁₉ H ₃₂ O ₂	0.06	9,12,15-Octadecatrienoic acid, methyl ester		✓	✓	✓	✓	✓
42	23.150	138.1047	156.1385	C ₉ H ₁₄ O	1.79	3,5,5-Trimethyl-2-cyclohexene-1-one (Isophorone)	✓	✓	✓	✓	✓	✓
43	23.150	155.1313	156.1385	C ₉ H ₁₇ NO	2.04	2,2,6,6-tetramethyl-4-Piperidinone	✓	✓	✓	✓	✓	✓
44	24.115	308.2723	309.2793	C ₂₀ H ₃₆ O ₂	2.46	9,12-Octadecadienoic acid ( Z , Z )-, ethyl ester		✓	✓	✓	✓	✓
45	24.634	278.2258	279.2330	C ₁₈ H ₃₀ O ₂	4.41	α -Linolenic acid		✓		✓		✓
46	24.634	396.3756	397.3829	C ₂₉ H ₄₈	0.12	Stigmastan-3,5-diene		✓	✓		✓	✓
47	24.716	264.2455	282.2793	C ₁₈ H ₃₂ O	0.79	( Z , Z , Z )-9,12,15-Octadecatrien-1-ol,	✓	✓	✓	✓		✓
48	24.716	281.2720	282.2793	C ₁₈ H ₃₅ NO	0.62	( Z ) -9-Octadecenamide	✓	✓	✓	✓	✓	✓
49	24.887	336.3030	354.3369	C ₂₂ H ₄₀ O2	0.61	Docosadienoic acid					✓	✓
50	25.154	370.3085	393.2981	C ₂₂ H ₄₂ O ₄	0.63	Hexanedioic acid, bis(2-ethylhexyl) ester	✓
51	25.240	412.3697	413.3774	C ₂₉ H ₄₈ O	− 2.09	(3 β )-Stigmasta-5,24(28)-dien-3-ol						✓

Note: ¹ RT = Retention time; ² m / z = Mass-to-charge ratio; ³ Mass error (ppm) = The difference between the experimentally measured mass of a molecule and its theoretical exact mass < ±5 ppm.

Table 4 Identified compounds from SPE1-SPE6 by LC-MS (negative mode).

No	RT (min) ¹	Mass	m / z (Expected) ²	Chemical Formular	Error (ppm) ³	Identification	Samples (SPE)
No	RT (min) ¹	Mass	m / z (Expected) ²	Chemical Formular	Error (ppm) ³	Identification	1	2	3	4	5	6
1	1.783	130.0626	175.061	C ₆ H ₁₀ O ₃	− 3.17	Propanoic acid, anhydride	✓	✓	✓	✓	✓	✓
2	9.243	93.0578	138.0561	C ₆ H ₇ N	− 0.24	2-Methylpyridine			✓	✓	✓
3	9.243	139.0632	138.0561	C ₇ H ₉ NO ₂	− 1.11	3-ethyl-4-1H-Pyrrole-2,5-dione			✓	✓	✓
4	10.611	116.0832	115.0762	C ₆ H ₁₂ O ₂	− 4.20	4-hydroxy-4-methyl-2-Pentanone	✓	✓	✓	✓	✓	✓
5	17.469	98.0731	157.0870	C ₆ H ₁₀ O	− 0.41	4-methyl-3-Penten-2-one	✓	✓	✓	✓	✓	✓
6	17.839	196.1092	241.1079	C ₁₁ H ₁₆ O ₃	− 3.54	Loliolide		✓		✓	✓	✓
7	17.847	138.1046	183.1029	C ₉ H ₁₄ O	1.06	3,5,5-trimethyl-2-Cyclohexene-1-one (Isophorone)	✓	✓	✓	✓	✓	✓
8	18.252	128.1204	173.1186	C ₈ H ₁₆ O	1.87	4-methyl-2-Heptanone	✓	✓	✓	✓	✓	✓
9	18.730	150.0682	209.0818	C ₉ H ₁₀ O ₂	1.02	4-ethenyl-2-methoxyPhenol				✓
10	19.383	180.1150	179.1079	C ₁₁ H ₁₆ O ₂	− 0.36	Dihydroactinidiolide	✓	✓	✓	✓	✓	✓
11	20.066	208.1463	207.1392	C ₁₃ H ₂₀ O ₂	− 0.16	4-(2,2,6-trimethyl-7-oxabicyclo [4.1.0] hept-1-yl)-3-Buten-2-one	✓	✓	✓	✓	✓	✓
12	20.190	220.1828	265.1810	C ₁₅ H ₂₄ O	0.46	2,6-bis(1,1-dimethylethyl)-4-methyl Phenol	✓	✓	✓	✓		✓
13	20.215	264.2091	309.2074	C ₁₇ H ₂₈ O ₂	0.72	7,10,13-Hexadecatrienoic acid, methyl ester	✓	✓	✓	✓	✓	✓
14	20.357	268.2403	313.2386	C ₁₇ H ₃₂ O ₂	0.35	( Z )-9-Hexadecenoic acid, methyl ester	✓	✓	✓	✓	✓	✓
15	20.425	228.2079	287.2226	C ₁₄ H ₂₈ O ₂	− 4.59	Tetradecanoic acid (Myristic acid)	✓	✓		✓	✓	✓
16	20.589	280.1867	339.2006	C ₁₇ H ₂₈ OS	2.16	1,5-Hexadien-3-ol, 3-methyl-6-(methylthio)-1-(2,6,6-trimethyl-1-cyclohexen-1-yl)	✓	✓	✓	✓	✓	✓
17	20.813	270.2558	315.2543	C ₁₇ H ₃₄ O ₂	− 0.24	Hexadecanoic acid, methyl ester	✓	✓	✓	✓	✓	✓
18	21.094	292.2393	351.2534	C ₁₉ H ₃₂ O ₂	− 3.14	9,12,15-Octadecatrienoic acid, methyl ester			✓	✓
19	21.266	232.1828	291.1968	C ₁₆ H ₂₄ O	0.57	1-Hexadecanol	✓	✓	✓	✓	✓	✓
20	21.489	242.2237	287.2230	C ₁₅ H ₃₀ O ₂	− 3.55	Pentadecanoic acid (Pentadecylic acid)	✓	✓	✓	✓	✓
21	21.815	270.2930	269.2856	C ₁₈ H ₃₈ O	2.65	1-Octadecanol	✓
22	21.903	294.2550	353.2688	C ₁₉ H ₃₄ O ₂	− 2.86	9,12-Octadecadienoic acid, methyl ester		✓
23	21.903	308.709	353.26902	C ₂₀ H ₃₆ O ₂	− 2.07	( Z , Z )-9,12-Octadecadienoic acid ethyl ester		✓
24	22.132	256.2398	315.2541	C ₁₆ H ₃₂ O ₂	− 1.54	12-methyl-Tetradecanoic acid, methyl ester	✓	✓	✓		✓	✓
25	22.132	212.2142	211.2066	C ₁₄ H ₂₈ O	0.66	Tetradecanal	✓	✓	✓	✓	✓	✓
26	22.616	182.2037	227.2016	C ₁₃ H ₂₆	1.36	2,5-dimethyl-2-Undecene	✓	✓	✓	✓	✓	✓
27	22.822	286.2509	285.2436	C ₁₇ H ₃₄ O ₃	0.25	2-hydroxy-Hexadecanoic acid, methyl ester	✓	✓	✓	✓	✓	✓
28	22.831	336.3747	395.3887	C ₂₄ H ₄₈	− 2.54	Cyclotetracosane						✓
29	23.093	330.2771	329.2699	C ₁₉ H ₃₈ O ₄	0.40	Hexadecanoic acid, 2-hydroxy-1-(hydroxymethyl) ethyl ester			✓		✓	✓
30	23.228	278.2248	277.2176	C ₁₈ H ₃₀ O ₂	0.93	α -Linolenic acid	✓	✓	✓	✓	✓	✓
31	23.854	281.2717	326.2699	C ₁₈ H ₃₅ NO	− 0.63	( Z )-Octa-9-decenamide				✓
32	23.989	296.3077	355.3217	C ₂₀ H ₄₀ O	− 0.70	(2 E ,7 R ,11 R )-3,7,11,15-tetramethyl-2-Hexadecen-1-ol	✓			✓
33	24.287	280.2406	279.2333	C ₁₈ H ₃₂ O ₂	1.25	Linoleic acid	✓	✓	✓	✓	✓	✓
34	24.287	234.2352	279.2334	C ₁₇ H ₃₀	1.88	( Z )-3-Heptadecen-5-yne	✓	✓		✓	✓	✓
35	24.393	324.3026	369.301	C ₂₁ H ₄₀ O ₂	− 0.64	4,8,12,16-tetramethyl-Heptadecan-4-olide				✓
36	24.393	370.3081	415.3069	C ₂₂ H ₄₂ O ₄	− 0.60	Hexanedioic acid, bis(2-ethylhexyl) ester				✓
37	24.753	264.2445	323.2584	C ₁₈ H ₃₂ O	− 3.08	( Z , Z , Z )-9,12,15-Octadecatrien-1-ol	✓	✓	✓	✓
38	24.827	336.3024	395.3161	C ₂₂ H ₄₀ O ₂	− 1.32	Docosadienoic acid		✓		✓	✓	✓
39	24.83	284.2717	343.2856	C ₁₈ H ₃₆ O ₂	0.75	Heptadecanoic acid, methyl ester	✓	✓	✓	✓		✓
40	24.83	298.2874	343.2856	C ₁₉ H ₃₈ O ₂	0.74	Octadecanoic acid, methyl ester	✓	✓	✓	✓	✓	✓
41	25.832	268.2767	327.2906	C ₁₈ H ₃₆ O	0.27	6,10,14-trimethyl-2-Pentadecanone	✓	✓	✓	✓	✓	✓
42	25.982	210.2343	255.2324	C ₁₅ H ₃₀	− 2.37	1-Pentadecene	✓	✓	✓	✓	✓	✓
43	25.982	196.2186	255.2324	C ₁₄ H ₂₈	− 2.54	Tetradecene	✓	✓	✓	✓	✓	✓
44	26.139	222.2351	281.2490	C ₁₆ H ₃₀	1.64	8-Hexadecyne	✓	✓	✓	✓	✓	✓
45	26.139	282.2563	281.2490	C ₁₈ H ₃₄ O ₂	1.34	( E )-9-Octadecenoic acid	✓	✓	✓	✓	✓	✓

Note: ¹ RT = Retention time; ² m / z = Mass-to-charge ratio; ³ Mass error (ppm) = The difference between the experimentally measured mass of a molecule and its theoretical exact mass < ±5 ppm.

Figure 5 The similar chemical composition of SPE1-SPE6 (positive mode).

Figure 6 The similar chemical composition of SPE1-SPE6 (negative mode).

Functional group analysis using FT-IR technique

FTIR analysis was performed qualitatively to confirm major functional groups and to compare overall spectral patterns among samples, providing supportive information consistent with LC-MS results. FTIR spectra profile of SPE1-SPE6 exhibited high similarity across all peaks, as shown in Figure 7 . The broad absorption band around 3,500 - 3,200 cm ⁻¹ is related to the presence of stretching vibrations of hydrogen bonds (hydroxyl groups -OH from carboxyls, phenols or alcohols). The peak at 2,800 - 3,000 cm⁻¹ corresponds to the methylene -CH ₂ and methyl -CH ₃ groups of long linear alkane components (the asymmetric and symmetric stretching in aliphatic components). The peak at 1,700 cm⁻¹ corresponds to the vibration of the C=O bond in the carbonyl group. In the FTIR spectra of SPE1-SPE6, several small and sharp peaks were detected between 1,600 and 1,000 cm⁻¹, corresponding to aromatic ring stretching (1,560 cm⁻¹), ionic carboxylate groups -COO⁻ (1420 cm⁻¹), C-O stretching (1,260 - 1,050 cm⁻¹), and C-O-C vibrations [40]. This evidence is consistent with the chemical compositions observed in all 6 Spirogyra species. SEP1-SEP6 displayed similar infrared (IR) spectra, likely reflecting the presence of common functional groups in the crude extracts. However, FTIR spectroscopy analysis shows only the functional groups in the crude extract and is insufficient to show the presence of different classes of chemical compositions [41].

Figure 7 FTIR spectra analysis of SPE1-SPE6.

Conclusions

This study demonstrates that combining morphological, rbc L-based molecular, and chemical analyses provides a comprehensive framework for characterizing Spirogyra species from Sakon Nakhon province. It was identified by using morphology and molecular biology as Spirogyra submaxima , S. fluviatilis , S. maxima , and S. chungkingensis . The rbc L gene can be used to study species identification but must be studied in combination with morphology. Molecular biology studies can help distinguish species with similar morphological characteristics. LC-MS analysis of 6 Spirogyra samples detected 60 compounds in both positive and negative ionization modes, among which thirty compounds were consistently present across all 6 samples. However, each of the 6 Spirogyra also contained distinct compounds, representing specific variations of the algae found in different locations. The integration of these approaches allows the differentiation of closely related species and reveals both shared and locality-specific metabolites. These findings suggest that chemical profiles may reflect environmental influences and species adaptation, offering insights into the ecological diversity of Spirogyra . Overall, this study establishes baseline knowledge that can guide future taxonomic, ecological, and biotechnological investigations, including the exploration of bioactive compounds for industrial applications.

Acknowledgements

This research was funded by the Thailand Science Research and Innovation (TSRI) and National Science, Research and Innovation (NSPF) or Fundamental Fund (FF) 2023, Sakon Nakhon Rajabhat University, Thailand.

Declaration of Generative AI in Scientific Writing

This manuscript used generative artificial intelligence (AI) tools, namely QuillBot for grammar checks. All scientific content, analysis, and conclusion were developed by authors.

CRediT Author Statement

Nopparut Sitthiwong : Conceptualization, Methodology, Supervision, Validation, Investigation, Funding acquisition, Project administration, Resources, and Writing –original draft. Kaewkanlaya Sotthisawad : Methodology, Data curation, Formal analysis, Investigation, Validation, Resources, and Visualization. Supakorn Arthan : Conceptualization, Resources, Methodology , Data curation, Validation , Investigation, Visualization Project Administration, Funding acquisition, and Writing – review & editing. Jeeraporn Pekkoh : Data curation, Formal analysis, Investigation, Validation, and Visualization. Kittiya Phinyo : Data curation, Formal analysis, Investigation, Validation, and Visualization.

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