# AutoZooMS: Integrating robotics into high-throughput ZooMS for the species identification of palaeontological remains at Grotte Mandrin, France

Ellie-May Oldfield, Mark S Dunstan, Manasij Pal Chowdhury, Ludovic Slimak, Michael Buckley

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## To cite this version:

Ellie-May Oldfield, Mark S Dunstan, Manasij Pal Chowdhury, Ludovic Slimak, Michael Buckley. AutoZooMS: Integrating robotics into high-throughput ZooMS for the species identification of palaeontological remains at Grotte Mandrin, France. Archaeological and Anthropological Sciences, 2024, 17 (1), pp.12. 10.1007/s12520-024-02073-7. halshs-05368450

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# AutoZooMS: Integrating robotics into high-throughput ZooMS for the species identification of palaeontological remains at Grotte Mandrin, France

Ellie-May Oldfield<fnref n="1" /> · Mark S. Dunstan<fnref n="1" /> · Manasij Pal Chowdhury<fnref n="1" /> · Ludovic Slimak<fnref n="2" /> · Michael Buckley<fnref n="1" />

Received: 31 March 2023 / Accepted: 9 September 2024

© The Author(s) 2024

## Abstract

As the vast majority of excavated palaeontological skeletal remains are fragmentary to the extent that they cannot be identified by morphological analysis alone, various molecular methods have been considered to retrieve information from an otherwise underutilised resource. The introduction of collagen fingerprinting, known as Zooarchaeology by Mass Spectrometry (ZooMS), has become one of the most popular approaches to improve taxonomic data yields from fragmentary bone. However, manual laboratory work remains a barrier to the analysis of larger sample numbers. Here we test the incorporation of liquid-handling robots to further develop ZooMS into a more automated technique using samples excavated from Grotte Mandrin, France. By increasing the faunal identifications of the morphological indeterminable remains at layer B2 (~42–44 Ka), from 55 to 1215 (1026 of which were processed via AutoZooMS), we identified a wider range of taxa, now including Ursidae and *Mammuthus*, as well as further hominin remains. AutoZooMS has the capacity to investigate larger proportions of palaeontological assemblages rapidly and cost effectively whilst requiring little human intervention, aiming to improve our understanding of the human past.

**Keywords** Palaeoproteomics · Zooarchaeology by mass spectrometry · Automation · Archaeofauna · Grotte Mandrin

## Introduction

The archaeological and palaeontological records are largely dominated by skeletal vertebrate remains, the biominer-alised tissues of bone and teeth that on average preserve for far longer than soft tissues (Briggs 2003). However, depending on the nature of the faunal assemblage, relatively large numbers of remains can be fragmentary at excavation before any taxonomic identification can take place. Bone fragmentation results from taphonomic processes includ-ing those of anthropogenic origin, such as dispersal and

accumulation via scavengers and floods or butchery by hominins (Noe-Nygaard 1977; Denys 2002). These frag-mentary remains are often unidentifiable by morphological analysis and can frequently make up to two thirds or more of excavated Pleistocene assemblages (Morin et al. 2017). Thus, fragmentation renders the majority of excavated bone taxonomically redundant and is a major driver of low taxonomic data yields from palaeontological assemblages. Increasing these data yields is of great interest not only to reduce the underutilisation of excavated faunal material and improve the efficiency of zooarchaeology and palaeobiol-ogy, but also to minimise any bias this large proportion of unidentifiable material has caused to current understandings of matters such as, the ecology of ancient environments and ancient hominin subsistence behaviours. The growing uptake of ancient biomolecular analysis in zooarchaeology and palaeobiology has begun to increase faunal taxonomic data yields, whereby several approaches have been used, including DNA-based (Newman et al. 2002; Kahila Bar-Gal et al. 2003) and proteomic-based methods (Buckley et al. 2009; Brandt et al. 2014; Presslee et al. 2017). The

<footnote>Michael Buckley
m.buckley@manchester.ac.uk</footnote>

<footnote n="1">¹ School of Natural Sciences, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK</footnote>

<footnote n="2">² Centre d'Anthropobiologie et de Génomique de Toulouse (CNRS UMR 5288), Université Paul Sabatier, Faculté de Santé, Bâtiment A, 37 allées Jules Guesde, Toulouse 31000, France</footnote>
latter of which is gaining in popularity due to its compromise between cost, success rate and taxonomic resolution in relation to DNA and lipids. Species identification by collagen fingerprinting, also known as Zooarchaeology by Mass Spectrometry (ZooMS) is the most promising of these.

## Zooarchaeology by mass spectrometry (ZooMS)

Bone and tooth dentine is dominated by type I collagen, a three-stranded molecule, with a triple helical structure (Brodsky and Ramshaw 1997). Tetrapods typically have two identical a1(I) chains and one a2(I) chain (Henriksen and Karsdal 2019). Both strands contain a repeated glycine-X-Y amino acid motif, in which amino acids X and Y are typically proline and hydroxyproline (Chu et al. 1984). This structure is highly conserved throughout many species, particularly in the a1(I) chain. However, the a2(I) strand is more genetically variable, with more distant relatives exhibiting greater variation from one another (Buckley 2018). This chain has a highly conserved charge distribution; as trypsin typically cleaves at the C-terminal of arginine and lysine (Olsen et al. 2004), the a2(I) chain exhibits conserved cleavage patterns during tryptic digestion. Nevertheless, while most published taxonomic biomarkers are a2(I) peptides, a small subset of published ZooMS taxonomic markers are peptides derived from the a1(I) chain (Buckley et al. 2009, 2016; Buckley 2018; Harvey et al. 2018, 2019a, b; Buckley and Cheylan 2020; Jensen et al. 2020; Dierickx et al. 2022; de Kock et al. 2023).

This has all allowed for the development of ZooMS (Buckley et al. 2009), a collagen peptide mass fingerprinting technique which can extract taxonomic data from fragmentary bone most often down to genus level (Buckley et al. 2017, 2019) and to species level in camels (Rybczynski et al. 2013), some rodents (Buckley et al. 2016, 2018), bats (Buckley and Herman 2019), reindeer (Rangifer tarandus is the only representative of the genus) (Buckley and Collins 2011), turtles (Harvey et al. 2019a), frogs (Buckley and Cheylan 2020) and fish (Rick et al. 2019; Hawkins et al. 2022), including the ability to identify hybrids (Marković et al. 2021). ZooMS was originally developed using the acid-insoluble collagen fraction (Buckley et al. 2009), while later methods utilise the soluble fraction (van der Sluis et al. 2014). Analysing the acid-insoluble fraction poses less contamination risk while the acid-soluble fraction can produce greater collagen yields, but both routes produce a similar number of peaks in their peptide mass fingerprints (Buckley 2018). Quality of collagen perseveration can impact which fraction is best analysed; Wang et al. 2021 report that samples exhibiting good collagen perseveration are more suited to analysis via the typical insoluble methodology, involving an ammonium bicarbonate buffer, while analysis via

the acid-soluble fraction shows increased success rates for specimens with low-medium collagen preservation. Generally, contamination is less of an issue in ZooMS than aDNA sequencing as there is no amplification involved (Pääbo et al. 2004; Buckley and Herman 2019).

The acid-soluble ZooMS methodology is currently performed via two operating procedures using either low-throughput (LT) (Buckley et al. 2009) and/or high-throughput (HT) approaches (Buckley et al. 2016). The LT method uses ultrafiltration into singular Eppendorf tubes, whereas the HT method uses 96-well microtiter ultrafilter plates. The LT method is better suited to processing precious specimens or low sample sizes due to risks relating to clogging the filters and cross-contamination, as well as yielding better quality spectra. HT workflows are much more time efficient and can process up to 384 samples at once (sample estimate based on ability to centrifuge four 96 well plates simultaneously, increased, or decreased centrifuge access would alter this estimation). However, since Buckley et al. introduced HT ZooMS in 2016 only a few other studies have analysed the spectra of ≥ 1000 samples (Brown et al. 2016, 2021b; Harvey et al. 2022; Sinet-Mathiot et al. 2023). This is because funding, time, and staff availability all heavily limit the uptake of ZooMS. Despite being more time efficient than LT, HT is still a manual process and requires substantial staff time, which is usually the costliest expense in a laboratory.

Processing large sample sizes has further benefit in palaeoecology than just increasing the utilisation of excavated faunal palaeontological material. ZooMS analyses are a looking glass through which we can view ancient ecosystems and by expanding the size of our analysis we expand our field of view into the past and thus the scope of our investigations. Larger sample sizes can approximate the actual population with greater precision and power than smaller sample sizes. For one, outliers, such as rare low abundance species, have a lesser impact on analyses in larger sample sizes (Wisz et al. 2008). This is important when interpreting ancient species abundance and diversity metrics. Secondly, larger sample sizes reduce the likelihood of false negative errors; power in research is typically regarded as the likelihood of rejecting the false null hypothesis, thus, larger sample sizes can increase the power of a study (Serdar et al. 2021). In palaeontological contexts this can be hypothesised as, larger sample sizes provide higher resolving power for species richness metrics as they reduce the likelihood of attaining false negatives for low abundance species; this is later observed in our analyses and has been reported in a variety of other publications (e.g. Broughton et al. 2011; Steele 2015; Woo et al. 2016). This is important as often ZooMS and other ancient proteomic analyses are used to make ecological inferences about ancient ecosystems. This
can include proposing phylogenies (Buckley 2013; Welker et al. 2015, 2017; Cappellini et al. 2019; Buckley et al. 2020), inferring anthropogenic effects on ancient biodiversity (Harvey et al. 2019a; Fossile et al. 2023), informing on hominin subsistence behaviours (Sinet-Mathiot et al. 2019, 2023; Torres-Iglesias et al. 2024) including tool manufacture (Ashby et al. 2015; McGrath et al. 2019; Martisius et al. 2020; Bradfield et al. 2021; Evans et al. 2023), and the development of animal husbandry (Janzen et al. 2021; Elnaggar et al. 2022). By only sampling a small proportion of excavated faunal material, outliers and uncontrollable sampling biases can heavily influence research outputs. Increasing sample sizes in ZooMS analyses will increase likelihood that research inferences are representative of the faunal population being studied.

There are further benefits of the uptake and evolution of high-throughput collagen fingerprinting technologies outside of the study of ancient life. Peptide mass fingerprinting can also be utilised in food and textile industries to screen for food fraud via the mislabelling of protein products (Carrera et al. 2011; Camerini et al. 2016; Ulrich et al. 2017; Christiansen et al. 2018; Azémard et al. 2021; Dierickx et al. 2023); in forensic applications for identifying the source of unknown biological material (Seraglia et al. 2004; Patel et al. 2015; Kamanna et al. 2017; Kennedy et al. 2022; Igoh et al. 2024) and monitoring for the illegal trade of animal products such as rhino horn (Solazzo et al. 2013; Fei et al. 2022).

## Grotte Mandrin, France

Grotte Mandrin is a rock shelter in the Rhône Valley, Drôme, France, overlooking the eastern bank of the middle Rhône River Valley. Mandrin's 3 m deep stratigraphic Pleistocene sequence has been broken down into 12 archaeological layers (J-B1), with radiocarbon dating placing the oldest layer at Marine Isotope Stage 5, with the younger layers being dated to the end of the middle Palaeolithic and the advent of the Upper Palaeolithic (Slimak et al. 2022; Metz et al. 2023).

This site has long been of interest in the study of European hominin evolution. Hominin fossils have been excavated across multiple layers of the site, including nine dental specimens and ~60,000 lithics (Slimak et al. 2022). These hominin artifacts are representative of both early anatomically modern human and Neanderthal occupations. Compared to other regions of the world, evidence of early modern human emergence in Europe out of Africa is relatively late, potentially due to well established Neanderthal occupation (Hublin 2015; Liu et al. 2015; Clarkson et al. 2017). At present Mandrin is home to the earliest documented anatomically modern humans in Europe, with the remains dated to 56,800–51,700 calibrated years before present (cal. B.P.)

using accelerator mass spectrometry (AMS) (Slimak et al. 2022). AMS radiocarbon dating, lithic analysis and dental morphology has helped reveal that throughout the last thousand years of the Neanderthal existence, multiple successive replacement hominin occupations occurred at Mandrin of both Neanderthal and anatomically modern human groups (Slimak et al. 2022). Therefore, Grotte Mandrin provides a valuable insight into a period in which Neanderthal and early modern humans coexisted in Europe.

Grotte Mandrin faces north towards the Mistral wind (Vandevelde et al. 2017). The Mistral is a cold and dry mesoscale wind that is found in the valleys of south-eastern France, it blows from the north through the Rhône Valley before eventually reaching the northwest coast of the Gulf of Lion, Mediterranean Sea (Obermann et al. 2018). The site sits at 225 m elevation, with little protection from the Mistral; many layers and large amounts of sediment has settled here, riding the tails of the strong wind into the shelter (Slimak et al. 2022). This sustained history of sediment deposit at the site has aided in the preservation of the large collection of archaeological material. Research at the site began in 1990 and the shelter has since undergone multiple excavations (Slimak et al. 2021, 2022, 2023; Metz et al. 2023). At present ~70,000 archaeofaunal remains have been excavated, a large proportion of which are fragmentary and unable to be identified by morphological analysis (this proportion varies with taxonomic level and the type of site, but typically 70–90% of recovered archaeological remains are not morphologically identified beyond family level). At Mandrin, of the upper layers only 11.4% of skeletal remains at layer B, 9.65% at layer C, 8.68% at layer D, and 5.35% at layer E have been morphologically determinable and at the lower layers even less so (Brugal 2021; Brugal et al. 2021). The extensive archaeofaunal assemblage at Grotte Mandrin is a great exemplary candidate for the need of an automated ZooMS technique given that a sizeable investment of time and funding has allowed for Grotte Mandrin to be excavated for the last three decades. In this study we describe one such automated ZooMS (hereafter AutoZooMS) approach that we use to help ensure maximal taxonomic data yields can be secured from this investment.

All samples in this study are sourced from Grotte Mandrin, layer B2, which dates to ~42,000–44,000 cal. B.P. and attributed to the Post-Neronian II culture, representing the end of the Middle Palaeolithic period and a time which is synonymous with the disappearance of Neanderthals in this region (Higham et al. 2014). Neanderthal remains have been excavated from this layer (Slimak et al. 2023), along with Mousterian technologies that are heavily associated with European Neanderthal occupation (Klein 1992). Mousterian technologies are recorded in four archaeological layers each composed of tens of thousands of lithics and fauna
recently excavated and robustly dated (Higham et al. 2014; Brugal 2021; Brugal et al. 2021; Slimak et al. 2021, 2022). Five distinct cultural phases can be recognised within the Grotte Mandrin archaeological sequence; Quina rhodanien (layer F), Neronian (layer E), Post-Neronian I (P NI, layer D), Post-Neronian II (PNII, layers C2 to B2), and Protoaurignacian (layer B1) (Slimak 2004; Vandevelde et al. 2017; Slimak et al. 2022). It is the only site in the Mediterranean region of France to exhibit such extensive coverage of these cultural transitions, the archaeological records at other Mousterian sites in France, such as Grotte de Néron (e.g. Veyrier et al. 1951), Baume Moula-Guercy (e.g. Defleur et al. 1999; Saos et al. 2014; Richards et al. 2023), Abri du Maras (e.g. Baudet et al. 1955; Moncel et al. 2021; Ruebens et al. 2022) and Grotte du Figuier (e.g. Slimak and Plisson 2008; Moncel et al. 2012) are interrupted after the Neronian (Slimak 2004; Slimak et al. 2022, 2023). Integrating the layers of these sites into a robust regional type aids and contextualises understanding of Neanderthal and early *Homo sapiens* existence across Mediterranean France. Layer Néron II corresponds to Mandrin F, Néron I to Mandrin E; the unfortunately heavily mixed collections of Maras I and I' correspond to Mandrin E, Moula IV to Mandrin E, etc. (Slimak 2004; Slimak et al. 2022). The upper levels of Néron, Maras, Moula, and Figuier are harder to categorise due to heterogenous radiometric dating, mixed stratigraphy, and thus mixed techno-cultural components (Slimak 2004; Slimak et al. 2022). Therefore, Grotte Mandrin provides the most extensive insight into the last expression of the Mousterian in Mediterranean France, the PNII (Higham et al. 2014). Evidence of the PNII has also been proposed at Grotte de Saint-Marcel (Szmidt et al. 2010), while this is

associated with few archaeological elements and two isolated AMS datations, this remains interesting as it is at present the only other trace of late Neanderthal communities outside of Grotte Mandrin in Mediterranean France.

For this study, samples were specifically sourced from a region of Grotte Mandrin, layer B2 named ‘Zone Thorin’ (ZT) (Fig. 1), after the Neanderthal individual, nicknamed Thorin, that was discovered in this area in 2015, a first in France since 1979 (Slimak et al. 2023). This area of the site requires very slow excavation as the body is highly fragmented in sand deposits, surrounded by thousands of fragmentary bones, of which surrounding fauna and Thorin are impossible to morphologically discriminate. To be a viable method of identifying whole palaeontological assemblages and to increase uptake of HT ZooMS in the scientific community, the efficiency and robustness of HT ZooMS needs to improve. The main aim of this study was to explore the inclusion of liquid handling robots into the ZooMS protocol, for improving accuracy, repeatability, and robustness (e.g., multichannel pipette and clogging error), hereafter referred to as ‘AutoZooMS’, which is the HT method adapted for automation. We hope that the fast-processing power of this technique will promote increased utilisation of excavated palaeontological material to increase taxonomic data yields, reduce underutilisation of archaeofaunal material and encourage larger sample sizes in future palaeontological research. AutoZooMS has been developed using the ancient faunal assemblage at layer B2-ZT, Grotte Mandrin, thus the secondary aim of this study was to conduct a ZooMS analysis of this region of the site. The fauna identified in this project have the potential to provide insight into Neanderthal subsistence behaviours due to their proximity

<figure><img src="image_3.png" /><figcaption>**Fig. 1** Map of Grotte Mandrin excavation area with location of sampling for AutoZooMS highlighted. The green outline represents the ~100 m² area of Grotte Mandrin which has been excavated from 1990-2023. The small pink rectangle highlights the spatial location of sam-</figcaption></figure>

ples processed via AutoZooMS in this study and represents an area of ~50 cm². The fragmentary bones recovered are a mix of piece-plotted and sieved material
to this discovery. Additionally, employing AutoZooMS at
this assemblage, has the potential to recognize additional
fragments of Thorin that can be used for biomolecular anal-
yses (DNA, isotopes, or dating) without having to perform
destructive techniques on potentially precious specimens
which confer specific information in terms of their morphol-
ogies and use-wear, such as teeth or other anthropologically
diagnostic material.

**Methods and materials**

Palaeontological samples

A total of 1707 samples were involved in the AutoZooMS
analysis of Grotte Mandrin, all from layer B2, which is
dated~42,000-44,000 cal. B.P. For comparison of success
rate and species richness, 96 samples were run via LT and
469 via HT. Detailed visual representation of the location
and stratigraphy of the site can be found in previously pub-
lished literature (Vandevelde et al. 2017). The fragmentary
bones processed in this study represent all the piece-plotted
material and a small portion of the sieved material of a spe-
cific region of layer B2, a~50 cm² area from multiple exca-
vations between 2018 and 2020 (Fig. 1). This small region
is named ‘Zone Thorin’ as it represents the immediate sur-
rounding area of the site of discovery of a late Neander-
thal individual, nicknamed Thorin (Slimak et al. 2023). All
piece-plotted samples from the excavation of this area were
processed and additional samples were randomly selected
from bulk bags of sieved material. The only exclusionary
measure applied was that very small bone fragments, less
than ~5 mm in length were not chosen, as very small bone
fragments, weighing below the advised 10-20 mg chip size
for ZooMS (Brown et al. 2020), readily float in acid and
thus pose a potential contamination risk as such material
could stick to a robotic pipette tip during the retrieval of the
acid-soluble fraction.

Reagents

LT, HT, and AutoZooMS used identical reagents: Hydro-
chloric acid (HCL) and sequencing-grade trypsin were
sourced from Fisher Chemicals (U.K.) and Promega (U.K.)
respectively. Acetonitrile (ACN), ammonium bicarbonate
(ABC), trifluoroacetic acid (TFA), a-cyano-4-hydroxycin-
namic acid (matrix) and mass spectrometric standards were
obtained from Sigma-Aldrich (U.K.).

Low-throughput protein extraction

Following an adapted protocol laid out by van der Sluis
et al. (2014) bone fragments were decalcified overnight in
1.5 mL Eppendorf tubes using 1 mL 0.6 M HCL. The next
day, 0.5 mL of acid was transferred into 10 kDa molecular
weight cut-off (MWCO) ultrafilters (Vivaspin, U.K.) and
centrifuged for 30 min at 12,400 rpm. After discarding flow
through 0.5 mL 50 mM ABC was added and the ultrafilters
were centrifuged. This process was conducted twice. The
acid-soluble collagen peptides were resuspended in 0.2 mL
50 mM ABC and transferred to 1.5 mL Eppendorf tubes.
Then 0.2 µg of trypsin was added to each sample. The solu-
ble collagen fragments were then left to digest overnight on
a heating block at 37 °C. 1 µL sample was then diluted in
19 µL 0.1% TFA. For each sample 1 µL of matrix solution
(10 mg/mL in 50% ACN/0.1% TFA) and 1 µL of diluted
sample was spotted onto a 384-well stainless steel MALDI
target plate and left to air dry.

High-throughput protein extraction

HT protein extraction was based on the protocol laid out by
Buckley et al. (2016). HT uses the same reagent volumes
and concentrations as LT, the procedure is adapted to a plate
processing format using multi-channel pipettes. Bones were
decalcified in 48-well Corning™ Costar™ cell culture-
treated flat-bottom plates. Soluble protein was filtered in 1
mL well 10 kDa MWCO 96-well ultrafiltration plates (Pall
Corporation). These ultrafiltration plates were sat on 1.1 mL
96-well deep well plates (Axygen) that collected any liq-
uid flow through during centrifugation. A microplate seal-
ing film (Sigma-Aldrich, U.K.) was then placed on top of
the ultrafiltration plate and the structure was taped together.
The plates were then centrifuged for 45 min at 23 °C, 3700
mph. Then two ABC washes were conducted. After the
last centrifuge, soluble protein was resuspended in 200 µL
ABC and 100 µL of sample was transferred into 2×96-well
Corning™ Costar™ cell culture-treated flat-bottom plates.
Overnight trypsin digest took place on a heating block at
37 °C (0.2 ug trypsin per sample). 40 µL of matrix solution
(10 mg/ml in 50% ACN/0.1% TFA) was added to a 1.1 mL
96-well deep well plate (Axygen), then 2 µL of sample was
mixed into the matrix solution. Finally, 2 µl of this mixture
was spotted onto a 384-well stainless steel MALDI target
plate and left to air dry.

Automated protein extraction

AutoZooMS was developed on Hamilton Method Editor for
the Hamilton Microlab STAR Liquid Handling System using
the multiprogramming software Venus. The method uses 50
µL, 300 µL and 1000 µL CO-RE Hamilton Tips and is split into 5 runnable methods (Fig. 2): 1 - HCl addition, 2 - HCl removal, 3 - filter plate wash and elute, 4 - trypsin addition and 5 - matrix addition and spotting. Reagent volumes differ slightly to HT. The machine used 8 individual pipetting channels to add 0.95 mL 0.6 M HCl to each 48-well plate and 1.9 mL to each 24-well plate. Then of this acid-soluble protein, 0.3 mL was removed and ultrafiltered; 10 kDa MWCO ultrafiltration plates were used during method optimisation and 30 kDa MWCO plates thereafter. A 96-channel Multi-Probe-Head (MPH) was used for ABC addition, sample resuspension and transfer to 96-well plates. Manual intervention was required at each centrifugal stage. Trypsin (0.2 µg trypsin per sample in initial testing, 0.1 µg in final method) was added using 8 individual channels. Manual intervention was required at this point to transfer samples to heating block. Matrix addition and mixing with sample was conducted using the 96 MPH head. All spotting was carried out using the 8 individual channels. Troughs of HCL, ABC and matrix provided the machine with the necessary reagent reservoirs. Trypsin was sourced from first column (A-H) of a PCR plate as the cone shaped bottom favours retrieval of small volumes.

Each method within the programme could be run inde-
pendently to allow multiple batches of samples to be pro-
cessed simultaneously to maximize the speed and number
of samples being processed, up to 3×384 batches could be

processed simultaneously. By reducing decalcification and
digestion time (Supplementary Fig. S1 & S2), it would be
feasible to process > 1000 samples within a 24-hour period.

Mass spectrometry

Collagen fingerprints were generated using a RapifleX
matrix assisted laser desorption/ionisation (MALDI) time
of flight (TOF) mass spectrometer (Brucker Daltonics, Ger-
many). Each spot was calibrated against a 1 in 10 diluted
MS standard which contained five known peptides. This
standard was made using a ProteoMass™ Protein MALDI-
MS Calibration Kit (Sigma-Aldrich, UK) following sup-
plier's protocol. MALDI-TOF mass spectra were acquired
by collecting up to 10,000 laser shots using a random walk
per sample spot, and peaks within the m/z range 1100-3200
were compared with those published previously (Buckley
and Collins 2011; Welker et al. 2016; Buckley et al. 2017)
for anticipated Middle Palaeolithic megafauna common to
Europe. In initial method testing, collagen fingerprinting
success was scored 0, 1 or 2 (Pothier Bouchard et al. 2019).
Spectra scoring 0 failed to produce any collagen biomarkers,
spectra scoring 1 exhibit one or more identifiable collagen
biomarker, species ID was able to be successfully assessed
from spectra scoring 2. Laboratory blanks were performed,
and spectra assessed. Both flexAnalyis v3.4 (Bruker Dal-
tonics, Germany) and mMass v5.5.0 (Strohalm et al. 2008,

<figure><img src="image_4.png" /><figcaption>**Fig. 2** AutoZooMS workflow. Hand symbol represents manual intervention, e.g., removing plates from machine and placing in centrifuge or incu-bator. \*Third and final ABC addition is used for resuspension in ABC</figcaption></figure>
2010; Niedermeyer and Strohalm 2012) were used for spectral analysis.

## Results

### Low throughput vs. high throughput vs. AutoZooMS

Once the AutoZooMS programme had been successfully developed, an initial test of 95 samples was analysed to compare the success and spectra quality of LT, HT, and AutoZooMS. This test was conducted to ensure the programme was not only functional but was as successful as the HT-ZooMS which it was adapted from. Each of the 95 samples were broken into three pieces and ran by LT, HT or AutoZooMS, and to ensure a fair comparison, all three runs maintained similar parameters in terms of trypsin concentration, decalcification length and digest length. The results of this comparison can be seen in Table 1 and Supplementary Fig. S3. AutoZooMS exhibited a collagen success rate of 62.1%, which was ~1.2x higher than its HT counterpart, and a species ID success rate of 50.5% which was ~1.5x higher than HT. LT did exhibit higher success rates than both HT and AutoZooMS which is to be expected. Thus, AutoZooMS provides a new alternate high-throughput methodology that is more successful at species identification than current HT techniques.

### AutoZooMS analysis of Grotte Mandrin, France

After the completion of all method optimisation, 1612 samples were processed using AutoZooMS and then manual species identification of collagen-yielding samples took place (e.g., Fig. 3A-B). This AutoZooMS analysis had a 60.7% species identification success rate (Supplementary Fig. S4), slightly higher than the 60% LT success rate seen in the AutoZooMS initial testing (Table 1). The sample to CHCA matrix spotting ratio was increased in the

<table><caption>**Table 1** Comparison of LT, HT and AutoZooMS collagen fingerprinting and species identification success</caption><thead><tr><th></th><th>Low Throughput</th><th>High Throughput</th><th>Auto-ZooMS</th></tr></thead><tbody><tr><td>Species Identification Success (n)</td><td>57</td><td>31</td><td>48</td></tr><tr><td>Species Uncertain Collagen Success (n)</td><td>14</td><td>18</td><td>11</td></tr><tr><td>Total Success (n)</td><td>71</td><td>49</td><td>59</td></tr><tr><td>Fail (n)</td><td>24</td><td>46</td><td>36</td></tr><tr><td>Collagen Success (%)</td><td>74.7</td><td>51.6</td><td>62.1</td></tr><tr><td>Identification Success (%)</td><td>60</td><td>32.6</td><td>50.5</td></tr></tbody></table>

AutoZooMS methodology after the initial test run, in the aim to improve AutoZooMS analyte to matrix signal to LT spectra standards; the higher AutoZooMS success rate in this large analysis indicates that these alterations were successful in improving AutoZooMS identification success. Analysis of the composition of the archaeofaunal assemblage at region B2-ZT of Grotte Mandrin (Fig. 3C) revealed further evidence of hominin occupation, with ~5% of the assemblage yielding a hominin signature. Such hominin remains are likely Neanderthal due to the sampling location, as previous Neanderthal remains have been confirmed via genomic analysis in the B2-ZT 50 cm² excavation region (Slimak et al. 2023). The layer was largely dominated by cervid (36.4% - cervidae excluding *Rangifer tarandus*), the majority of which will likely be *Cervus elaphus* (due to the period, location and previous morphological identifications at the site (Brugal 2021)) followed by equine (19.4%) and Suidae (13.2%); a total of 11 taxonomic groups were identified, including, Elephantidae (*Mammuthus* as per period and geographical location), bovine (*Bos/Bison*), *Rangifer tarandus*, caprine (*Ovis/Capra*), lagomorph (*Oryctolaglus/Lepus*), Suidae, Rhinocerotidae (*Coelodonta* as per period and geographical location), equine, hominin, cervid and Ursidae (Fig. 3C). Glutamine deamidation at *m/z* 1105.6 was assessed and all spectra yielding a *m/z* 1105.6 peak exhibited a partially deamidated signal (Supplementary Fig.S5).

### Effect of sample size on species abundance and richness

To assess the impact ZooMS sample size has on our understanding of the composition of palaeontological assemblages, the species abundance and richness of 96 LT samples, 469 HT samples and 1707 AutoZooMS samples were compared (Fig. 4; Table 2; Supplementary Fig. S6 & Table S1). Results showed that by facilitating the processing of larger sample sizes AutoZooMS provides higher resolving power to identify less abundant species such as *Mammuthus*; LT exhibited the lowest species richness and AutoZooMS the highest. Assemblage composition does differ between the three data sets, with notable differences including the percentage of *Sus*, which in HT analyses comprised only 4.7% of the identifiable assemblage whereas in AutoZooMS and LT it was 13.5% and 17.2% respectively. Differences in equine identification was also notable, composing 33.0% in HT analyses, despite only making up 20.7% and 19.0% of LT and AutoZooMS analysis. Similarly, lagomorph appears to make up 4.7% in HT but only 1.7% and 1.3% in LT and AutoZooMS. *Coelodonta* was not present at all in LT analysis but was present at 0.5% and 1.1% of the assemblage in HT and AutoZooMS respectively. Overall, we observed notable differences caused by subsampling from the B2-ZT
<figure><img src="image_5.png" /><figcaption>**Fig. 3** AutoZooMS analysis of Grotte Mandrin, France. **A** and **B** – AutoZooMS collagen fingerprints of lagomorph (**A**) and hominin (**B**). **C** – The composition of the palaeontological assemblage at layer B2-ZT, Grotte Mandrin, as determined by species identification success</figcaption></figure>

via AutoZooMS (n=978). Composition: Bovine – 12.9%, Caprine – 2.7%, Cervid – 36.4%, Coelodonta – 1.1%, Equine – 19.4%, Hominin – 4.9%, Lagomorph – 1.2%, *Mammuthus* – 0.2% Suidae–13.2%, *Rangifer*– 4.6% and Ursidae – 3.4% (to 1 d.p.)
<figure><img src="image_6.png" /><figcaption>**Table 2** Species identification success and species richness for LT, HT and AutoZooMS</figcaption></figure>

<table><thead><tr><th></th><th>Low Throughput</th><th>High Throughput</th><th>Auto-ZooMS</th></tr></thead><tbody><tr><td>Total Sampled</td><td>96</td><td>469</td><td>1707</td></tr><tr><td>Species ID Success (%)</td><td>60.4</td><td>40.7</td><td>60.1</td></tr><tr><td>Uncertain (%)</td><td>14.6</td><td>10.0</td><td>0.64</td></tr><tr><td>Species Richness</td><td>9</td><td>10</td><td>11</td></tr></tbody></table>

Grotte Mandrin archaeofaunal assemblage, particularly the lack of resolving power to identify lower abundance species such as *Mammuthus* and *Coelodonta*.

# Discussion

## Automated ZooMS

The overriding focus of this study was to develop an automated method of extracting taxonomic data from

palaeontological assemblages with the aims to increase taxonomic data yields and reduce current underutilisation of the fragmentary portion of assemblages. Our AutoZooMS approach was shown to be more successful than traditional HT collagen fingerprinting for species identification, yielding the potential to increase acquisition of large-scale ZooMS datasets (e.g., Buckley et al. 2016; Brown et al. 2021b, 2016; Sinet-Mathiot et al. 2023). Across a five-day working week, AutoZooMS could process ~2000 samples with under 1-hour human intervention each day; all 1612 samples used in this analysis were spotted in <3 h with 30 min of human intervention. If transferred to a fully automated system (that includes centrifugation and incubation), using parameters laid out in the supplementary for reducing decalcification and digestion time (Supplementary Fig. S1 & S2), >1000 samples could be fully processed in 24 h. As this study has a technology focus, spectra were manually analysed to ensure thorough assessment of AutoZooMS outputs, however, integrating AutoZooMS with automated species identification programmes would further reduce the
time and increase the efficiency of ZooMS analysis of large
datasets (Gu and Buckley 2018; Baker et al. 2023; Végh and
Douka 2024). Other factors to consider would relate to cost
and laboratory consumable efficiency; as protein preserva-
tion varies between sites, when working with a sample set
from a new site it would be beneficial to evaluate protein
abundance and establish optimum protease to protein ratios
before any ZooMS analysis takes place, in this case 20 µg
trypsin/0.4 mL buffer.

In initial testing, a discrepancy between the success of LT
and HT/AutoZooMS methodology was noted (Table 1; Sup-
plementary Fig.S3). It was postulated that this was related
to the different spotting techniques employed in LT and HT
methodologies. Protein digests are diluted in 0.1% TFA and
then spotted at a 1:1 ratio with 10 mg/mL CHCA matrix
(50% ACN, 0.1% TFA) in LT methodology, however, in HT,
protein digests are diluted directly into 10 mg/mL CHCA
matrix (50% ACN, 0.1% TFA), and then this mixture is spot-
ted. Therefore, there is an increased CHCA matrix to protein
digest spotting ratio in HT methodology compared to LT.
In the final AutoZooMS run, the volume of protein digest
diluted into CHCA matrix was increased and the species
identification success rate of this run was ~10% higher than
initial testing, bringing the species identification success
rate up to the LT success rate seen in initial testing (Table 1;
Supplementary Fig. S3 & S4). Success rates in methods
utilising ultrafilter plates can also be affected by clogging
issues, if this issue is prevalent, to reduce the amount of
loose particulates present, the acid soluble fraction could be
transferred to a 96-well deep well plate and centrifuged prior
to ultrafiltration. Potential further options to ensure all HT
and AutoZooMS processing success rates and spectra qual-
ity are comparable to LT, would be to dilute protein digests
1/20 into a 96-well plate containing 0.1% TFA and then
spot this mixture 1:1 with CHCA matrix, as to implement
a more unanimous operating procedure across the methods.
Neither of these options were explored here due to concerns
on increased plastic waste but are an option if acid-soluble
samples are particulate rich or if using an increased volume
of protein digest is undesirable. If there is a pre-established
consensus that the samples of interest exhibit low acid-solu-
ble collagen preservation the AutoZooMS method could be
adapted to process the insoluble fraction easily, this would
also circumvent ultrafilter plate clogging issues as this step
is not employed in the processing of the insoluble fraction.
Furthermore, while not employed in LT methodology in this
paper, C18 resin ZipTip pipette tips are commonly used in
LT ZooMS to purify, desalt, and concentrate protein sam-
ples and while not always necessary, this step can be use-
ful to improve spectra quality of complex/'dirty' samples.
A promising potential avenue for AutoZooMS methodol-
ogy would be to incorporate a robotically operated ZipTip

purification step which has proved successful in a recently
published ZooMS study (Fossile et al. 2023). Such incorpo-
ration would help ensure unanimous processing potential of
LT and AutoZooMS technologies for all steps of the ZooMS
sample processing, including optional purification stages.

Another use of robotics in paleoproteomics was recently
published by Rüther et al. (2022), who presented a short-
ened LC-MS/MS process, proposing that up to 200 samples
could be analysed per day. By comparison, ZooMS can
readily process ~2x this, but is more taxonomically limited
relative to LC-based methods. LC-MS/MS approaches have
been used for decades to identify the species and protein
composition of a sample, to greater accuracy than ZooMS,
but the taxonomic resolution is more closely linked to amino
acid sequences present in the search databases. For exam-
ple, Mackie et al. (2018) were able to distinguish between
chicken and duck egg white from a 14th century Italian
wall painting whereas due to the highly conserved nature
of bird collagen and the coincidental matching of peptides
with different amino acid sequences, such taxonomic sepa-
ration would be difficult to confidently achieve with ZooMS
peptide mass fingerprinting (Buckley 2018). However, due
to funding constraints in archaeology, AutoZooMS is more
suited to the mass processing of whole palaeontological
assemblages than LC-MS/MS approaches are. In terms of
the applicability of robotics to paleoproteomic research, it
is promising that Rüther et al. (2022) reported that manual
and robotic processing of samples via the SPIN protocol
exhibited similar peptide identification numbers and that
improved reproducibility was observed with the use of
robotics.

Archaeofaunal assemblage of Grotte Mandrin,
France

The composition of layer B2-ZT deduced in this study is
similar to that of previous manual ZooMS analysis of layer
B2, whereby Pal Chowdhury et al. (2019) reported that
*Cervus elaphus* (29%), bovine (20%) and equine (21%)
made up most of the layer B2 assemblage (n=70) with a
small proportion of *Suidae* (1%), but no *Mammuthus*, hom-
inin, Ursidae, or lagomorph were recorded in their study.
Of course, the presence of hominin in our investigation is
highly influenced by the sampling location within B2-ZT.
No data on the morphologically identifiable portion of
B2-ZT has been published specifically, however, of B2 gen-
erally, Brugal (2021), reported that the faunal composition
(n=615) was dominated by equine, cervid and lagomorph.
Increased diversity of carnivora taxa was morphologically
recorded in layer B2 compared to AutoZooMS and previ-
ous ZooMS analysis (Pal Chowdhury et al. 2019), with
*Lynx*, *Crocuta*, *Vulpes*, *Canis*, *Martes* and *Felis* all being
recorded, in addition to *Aves* and *Marmota* (Brugal 2021). *Mammuthus* was not identified in published morphological analysis in the B2 layer. Such morphological identifications, have not, as far as we are aware been counter analysed via ZooMS.

A study analysing the agreement of ZooMS and morphological identifications of identical specimens has recently been published (Morin et al. 2023). It was reported that over three sites the taxonomic profiles were statistically indistinguishable, but that morphologically less distinctive skeletal elements such as ribs were more prone to morphological identification error. Furthermore, morphological identification errors were more common with rarer species, potentially due to conservative analysts overlooking rarer, less probable taxonomic assignments (Driver 1992; Wolverton 2013; Morin et al. 2023), which could contribute to the lack of *Mammuthus* morphological identifications at Grotte Mandrin B2. A limitation of ZooMS is that it produces highly inflated number of identified specimen counts (NISP) compared to traditional morphological analysis (Buckley et al. 2017; Sinet-Mathiot et al. 2019). NISP counts are a measure of species abundance and as specimens processed via ZooMS are often highly fragmentary and disconnected from skeletal elements, it lends itself to an overinflated assumption that each fragment represents a unique individual, creating bias in our understanding of these sites. This would otherwise be mitigated by the more ideal zooarchaeological measure of minimum number of individuals (MNI) relying on using unique skeletal (including sided) elements. Such overinflation is especially true for larger fauna (Sinet-Mathiot et al. 2019, 2023; Morin et al. 2023), whereas skeletal elements from smaller fauna are typically less fragmented (Morin 2010), thus creating the potential for the over inflation of larger fauna NISP (%) in ZooMS analysis which can relatively dampen the NISP (%) of smaller fauna and bias understanding of assemblages. The large proportion of lagomorph morphologically identified at layer B2 compared to the relatively small proportion identified via AutoZooMS highlights this.

When comparing the species richness and abundance of B2-ZT Grotte Mandrin, via LT, HT and AutoZooMS (Fig. 4; Table 2; Supplementary Tables 1 & Fig.S6), increased species richness was observed in AutoZooMS compared to HT, and in AutoZooMS and HT compared to LT; in addition to notable differences in species abundance metrics (NISP %) of the assemblage. Processing larger sample sizes in this study provided the resolving power to identify lower abundance species such as *Mammuthus* and *Coelodonta*, similar conclusions on the effects of sample size on species richness in zooarchaeological research have been drawn previously (e.g. Broughton et al. 2011; Steele 2015; Woo et al. 2016). AutoZooMS presented here, provides an avenue

through which larger sample sizes can be processed more efficiently and therefore will hopefully be a valuable tool for the mass processing of palaeontological material, which can reduce sub-sampling biases and artifacts, and provide a more accurate picture of the fragmentary portion of archaeofaunal assemblages and thus ancient environments. It is worth noting that sample size does not inherently buffer the effects of sampling bias, in fact larger sample sizes can magnify biases in the face of erroneous experimental design (Kaplan et al. 2014). It is important that proper sampling methodology is followed that is conducive with the aims of assemblage analysis and the inference scope of such sampling is properly communicated. There is an array of literature on such sampling methods to avoid biases (e.g. Lyman' and Ames 2004; Peres 2010; Poteate and Fitzpatrick 2013). Of course, for reasons previously discussed, ZooMS analysis inherently imparts its own bias onto understandings of ancient assemblages. While not explored in this study, it is essential that with the hopeful increased uptake of larger scale ZooMS processing, future work continues to innovate methods of ZooMS and morphological data integration to better contextualise ZooMS findings (e. g. Sinet-Mathiot et al. 2019; Pothier Bouchard et al. 2020; Silvestrini et al. 2022).

As all bones processed here were from the same~50 cm² region it was to be expected that the partial glutamine deamidation signal witnessed at *m/z* 1105.6 was homogeneous across all collagen-yielding samples (Supplementary Fig. S5). Glutamine deamidation is routinely used as a signal of collagen degradation (van Doorn et al. 2012; Wilson et al. 2012; Chen et al. 2019; Ramsøe et al. 2020), however as a variety of environmental and anthropogenic factors can influence this (Schroeter and Cleland 2016; Pal Chowdhury et al. 2019), caution is recommended when attempting to attain inferences on chronological age or relative decay metrics (Brown et al. 2021a). Still, the ability to process large sample numbers via AutoZooMS could be of benefit to identify outliers within a supposedly homogenous sample set; such benefit could apply to assemblages excavated from sites with poor stratigraphic layer differentiation. It is unlikely that that glutamine deamidation could provide reliable evidence of anthropogenic impact via cooking (Brown et al. 2021a), even though the exposure of collagen to low temperatures during cooking does cause the protein to denature (Faillace et al. 2020).

The additional hominin remains identified in this study further support previous evidence of hominin occupations in Grotte Mandrin (Vandevelde et al. 2017; Metz et al. 2023; Slimak et al. 2023). Due to the sampling proximity to hominin remains, the ZooMS taxonomic data presented could provide insight into hominin subsistence behaviours. The high proportion of fragmentary bone could be
representative of hominin processing for subsistence or tool
making, i.e., bones being purposefully broken into smaller
pieces. Traces of such anthropogenic processes have been
morphologically recorded on 198 bone specimens in the
B2 layer in the form of microscopic striations and impacts
(Brugal 2021). With regards carnivore activity, 98 traces
have been recorded (Brugal 2021). Furthermore, 15.8% of
bones excavated from layer B2 appear burnt (Brugal 2021).
In addition to the thousands of excavated lithics from this
area, such zooarchaeological evidence supports the notion
that anthropogenic processing of faunal material is evident
at layer B2 (and others) at Grotte Mandrin - further discus-
sion on the zooarchaeological evidence of anthropogenic
food processing at Grotte Mandrin can be found in the
literature (Burgal, 2021). Hominin food processing could
also be contributing to the failure rate of the ZooMS analy-
sis presented in this study, as heat applied to bone during
cooking would be expected to increase collagen degrada-
tion (Faillace et al. 2020). Moreover, this anthropic frag-
mentation could be distinct between the different species
exploited as related to hominin choices and traditions. For
example, bone marrow extraction that induces an anthropic
fragmentation of the bone could be limited to specific spe-
cies; such inferences have been observed in *Bos/Bison* at the
hominin occupation site, Fumane cave, Italy (Sinet-Mathiot
et al. 2019). The relatively large representation of hominin
bones found in this ZooMS analysis is biased by the chosen
sample location that was focused in the direct vicinity of
Neanderthal skeletal remains (Slimak et al. 2023). As the
palaeontological sample set is specific to a particular spatial
area of the site, we cannot infer that 5% of the fragmentary
bones from throughout this layer (B2) belong to Neander-
thal remains. The deployment of this high-throughput Auto-
ZooMS method in the same layer but across a wider region
of the site, once excavated, might then be highly informa-
tive on improved understanding of the cultural anthropol-
ogy of these societies.

Conclusions

To conclude, presented here is a new AutoZooMS meth-
odology, that is just as, if not more successful than prior
HT ZooMS methods. This technique can process thousands
of samples in a couple of days at relatively lost cost, thus
making it a great technique to scan whole palaeontological
assemblages. We have applied this technique to the assem-
blage at layer B2-ZT, Grotte Mandrin, thus increasing the
taxonomic data yield from the morphological indetermin-
able remains at this layer, from 55 to 1215 (1026 of which
were processed via AutoZooMS). By processing larger sam-
ple sizes which confer increased resolving power to identify
low abundance species, we have identified a wider range

of taxa, now including *Mammuthus*. Additional hominin
remains have also been identified. With advances in method
development such as proposed here, more ZooMS analyses
could be subsequently carried out using much larger sample
sizes that are more truly representative of the contemporary
ecology, or at least reducing one of the main taphonomic
biases to such interpretation of ancient life.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s12520-0-24-02073-7.

**Author contributions** E.M.O., M.P.C. and M.D. carried out laboratory analyses. L.S. provided samples. E.M.O. and M.B. wrote the main manuscript text. E.M.O, M.D. and L.S. prepared Figs. 1, 2, 3 and 4. All authors reviewed the manuscript.

**Funding** We acknowledge support from the Henry Royce Institute for Advanced Materials (funded through EPSRC grants nos. EP/R00661X/1, EP/S019367/1, EP/P025021/1 and EP/P025498/1) for access to their facilities.

**Data availability** Raw mzXML files are to be made available on Figshare (10.6084/m9 figshare.27091483).

Declarations

**Competing interests** The authors declare no competing interests.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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